diff --git a/README.md b/README.md index 836018da009cf2dc2309c24c371c841f0740391b..1df474ce1c478d7cc5c8957def8f2bec9ebc308e 100644 --- a/README.md +++ b/README.md @@ -10,240 +10,242 @@ IxRT（Iluvatar CoreX RunTime）是天数智芯自研的高性能推理引擎， DeepSparkInference将按季度进行版本更新，后续会逐步丰富模型类别并拓展大模型推理。 +## LLM (Large Language Model) + +| Model | vLLM | TRT-LLM | TGI | +|-------------------------------|-------------------------------------------------------------------------|--------------------------------------------------------|---------------------------------------------------------------------------| +| Baichuan2-7B | [✅](models/nlp/large_language_model/baichuan2-7b/vllm) | | | +| ChatGLM-3-6B | [✅](models/nlp/large_language_model/chatglm3-6b/vllm) | | | +| ChatGLM-3-6B-32K | [✅](models/nlp/large_language_model/chatglm3-6b-32k/vllm) | | | +| DeepSeek-R1-Distill-Llama-8B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm) | | | +| DeepSeek-R1-Distill-Llama-70B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-1.5B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-7B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-14B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-32B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm) | | | +| Llama2-7B | [✅](models/nlp/large_language_model/llama2-7b/vllm) | [✅](models/nlp/large_language_model/llama2-7b/trtllm) | | +| Llama2-13B | | [✅](models/nlp/large_language_model/llama2-13b/trtllm) | | +| Llama2-70B | | [✅](models/nlp/large_language_model/llama2-70b/trtllm) | | +| Llama3-70B | [✅](models/nlp/large_language_model/llama3-70b/vllm) | | | +| Qwen-7B | [✅](models/nlp/large_language_model/qwen-7b/vllm) | | | +| Qwen1.5-7B | [✅](models/nlp/large_language_model/qwen1.5-7b/vllm) | | [✅](models/nlp/large_language_model/qwen1.5-7b/text-generation-inference) | +| Qwen1.5-14B | [✅](models/nlp/large_language_model/qwen1.5-14b/vllm) | | | +| Qwen1.5-32B Chat | [✅](models/nlp/large_language_model/qwen1.5-32b/vllm) | | | +| Qwen1.5-72B | [✅](models/nlp/large_language_model/qwen1.5-72b/vllm) | | | +| Qwen2-7B Instruct | [✅](models/nlp/large_language_model/qwen2-7b/vllm) | | | +| Qwen2-72B Instruct | [✅](models/nlp/large_language_model/qwen2-72b/vllm) | | | +| StableLM2-1.6B | [✅](models/nlp/large_language_model/stablelm/vllm) | | | + ## Computer Vision ### Classification -| Model | Prec. | IGIE | IxRT | -|------------------------|-------|-----------------------------------------------------------------------|-----------------------------------------------------------------------| -| AlexNet | FP16 | [✅](models/cv/classification/alexnet/igie/README.md#fp16) | [✅](models/cv/classification/alexnet/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/alexnet/igie/README.md#int8) | [✅](models/cv/classification/alexnet/ixrt/README.md#int8) | -| CLIP | FP16 | [✅](models/cv/classification/clip/igie/README.md#fp16) | | -| Conformer-B | FP16 | [✅](models/cv/classification/conformer_base/igie/README.md#fp16) | | -| ConvNeXt-Base | FP16 | [✅](models/cv/classification/convnext_base/igie/README.md#fp16) | [✅](models/cv/classification/convnext_base/ixrt/README.md#fp16) | -| ConvNext-S | FP16 | [✅](models/cv/classification/convnext_s/igie/README.md#fp16) | | -| ConvNeXt-Small | FP16 | [✅](models/cv/classification/convnext_small/igie/README.md#fp16) | [✅](models/cv/classification/convnext_small/ixrt/README.md#fp16) | -| CSPDarkNet53 | FP16 | [✅](models/cv/classification/cspdarknet53/igie/README.md#fp16) | [✅](models/cv/classification/cspdarknet53/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/cspdarknet53/ixrt/README.md#int8) | -| CSPResNet50 | FP16 | [✅](models/cv/classification/cspresnet50/igie/README.md#fp16) | [✅](models/cv/classification/cspresnet50/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/cspresnet50/ixrt/README.md#int8) | -| DeiT-tiny | FP16 | [✅](models/cv/classification/deit_tiny/igie/README.md#fp16) | [✅](models/cv/classification/deit_tiny/ixrt/README.md#fp16) | -| DenseNet121 | FP16 | [✅](models/cv/classification/densenet121/igie/README.md#fp16) | [✅](models/cv/classification/densenet121/ixrt/README.md#fp16) | -| DenseNet161 | FP16 | [✅](models/cv/classification/densenet161/igie/README.md#fp16) | [✅](models/cv/classification/densenet161/ixrt/README.md#fp16) | -| DenseNet169 | FP16 | [✅](models/cv/classification/densenet169/igie/README.md#fp16) | [✅](models/cv/classification/densenet169/ixrt/README.md#fp16) | -| DenseNet201 | FP16 | [✅](models/cv/classification/densenet201/igie/README.md#fp16) | [✅](models/cv/classification/densenet201/ixrt/README.md#fp16) | -| EfficientNet-B0 | FP16 | [✅](models/cv/classification/efficientnet_b0/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b0/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/efficientnet_b0/ixrt/README.md#int8) | -| EfficientNet-B1 | FP16 | [✅](models/cv/classification/efficientnet_b1/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b1/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/efficientnet_b1/ixrt/README.md#int8) | -| EfficientNet-B2 | FP16 | [✅](models/cv/classification/efficientnet_b2/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b2/ixrt/README.md#fp16) | -| EfficientNet-B3 | FP16 | [✅](models/cv/classification/efficientnet_b3/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b3/ixrt/README.md#fp16) | -| EfficientNet-B4 | FP16 | [✅](models/cv/classification/efficientnet_b4/igie/README.md#fp16) | | -| EfficientNetV2 | FP16 | [✅](models/cv/classification/efficientnet_v2/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_v2/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/efficientnet_v2/ixrt/README.md#int8) | -| EfficientNetv2_rw_t | FP16 | [✅](models/cv/classification/efficientnetv2_rw_t/igie/README.md#fp16) | [✅](models/cv/classification/efficientnetv2_rw_t/ixrt/README.md#fp16) | -| EfficientNetv2_s | FP16 | [✅](models/cv/classification/efficientnet_v2_s/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_v2_s/ixrt/README.md#fp16) | -| GoogLeNet | FP16 | [✅](models/cv/classification/googlenet/igie/README.md#fp16) | [✅](models/cv/classification/googlenet/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/googlenet/igie/README.md#int8) | [✅](models/cv/classification/googlenet/ixrt/README.md#int8) | -| HRNet-W18 | FP16 | [✅](models/cv/classification/hrnet_w18/igie/README.md#fp16) | [✅](models/cv/classification/hrnet_w18/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/hrnet_w18/ixrt/README.md#int8) | -| InceptionV3 | FP16 | [✅](models/cv/classification/inception_v3/igie/README.md#fp16) | [✅](models/cv/classification/inception_v3/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/inception_v3/igie/README.md#int8) | [✅](models/cv/classification/inception_v3/ixrt/README.md#int8) | -| Inception_ResNet_V2 | FP16 | | [✅](models/cv/classification/inceptionresnetv2/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/inceptionresnetv2/ixrt/README.md#int8) | -| Mixer_B | FP16 | [✅](models/cv/classification/mlp_mixer_base/igie/README.md#fp16) | | -| MNASNet0_5 | FP16 | [✅](models/cv/classification/mnasnet0_5/igie/README.md#fp16) | | -| MNASNet0_75 | FP16 | [✅](models/cv/classification/mnasnet0_75/igie/README.md#fp16) | | -| MobileNetV2 | FP16 | [✅](models/cv/classification/mobilenet_v2/igie/README.md#fp16) | [✅](models/cv/classification/mobilenet_v2/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/mobilenet_v2/igie/README.md#int8) | [✅](models/cv/classification/mobilenet_v2/ixrt/README.md#int8) | -| MobileNetV3_Large | FP16 | [✅](models/cv/classification/mobilenet_v3_large/igie/README.md#fp16) | | -| MobileNetV3_Small | FP16 | [✅](models/cv/classification/mobilenet_v3/igie/README.md#fp16) | [✅](models/cv/classification/mobilenet_v3/ixrt/README.md#fp16) | -| MViTv2_base | FP16 | [✅](models/cv/classification/mvitv2_base/igie/README.md#fp16) | | -| RegNet_x_16gf | FP16 | [✅](models/cv/classification/regnet_x_16gf/igie/README.md#fp16) | | -| RegNet_x_1_6gf | FP16 | [✅](models/cv/classification/regnet_x_1_6gf/igie/README.md#fp16) | | -| RegNet_y_1_6gf | FP16 | [✅](models/cv/classification/regnet_y_1_6gf/igie/README.md#fp16) | | -| RepVGG | FP16 | [✅](models/cv/classification/repvgg/igie/README.md#fp16) | [✅](models/cv/classification/repvgg/ixrt/README.md#fp16) | -| Res2Net50 | FP16 | [✅](models/cv/classification/res2net50/igie/README.md#fp16) | [✅](models/cv/classification/res2net50/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/res2net50/ixrt/README.md#int8) | -| ResNeSt50 | FP16 | [✅](models/cv/classification/resnest50/igie/README.md#fp16) | | -| ResNet101 | FP16 | [✅](models/cv/classification/resnet101/igie/README.md#fp16) | [✅](models/cv/classification/resnet101/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/resnet101/igie/README.md#int8) | [✅](models/cv/classification/resnet101/ixrt/README.md#int8) | -| ResNet152 | FP16 | [✅](models/cv/classification/resnet152/igie/README.md#fp16) | | -| | INT8 | [✅](models/cv/classification/resnet152/igie/README.md#int8) | | -| ResNet18 | FP16 | [✅](models/cv/classification/resnet18/igie/README.md#fp16) | [✅](models/cv/classification/resnet18/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/resnet18/igie/README.md#int8) | [✅](models/cv/classification/resnet18/ixrt/README.md#int8) | -| ResNet34 | FP16 | | [✅](models/cv/classification/resnet34/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/resnet34/ixrt/README.md#int8) | -| ResNet50 | FP16 | [✅](models/cv/classification/resnet50/igie/README.md#fp16) | [✅](models/cv/classification/resnet50/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/resnet50/igie/README.md#int8) | | -| ResNetV1D50 | FP16 | [✅](models/cv/classification/resnetv1d50/igie/README.md#fp16) | [✅](models/cv/classification/resnetv1d50/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/resnetv1d50/ixrt/README.md#int8) | -| ResNeXt50_32x4d | FP16 | [✅](models/cv/classification/resnext50_32x4d/igie/README.md#fp16) | [✅](models/cv/classification/resnext50_32x4d/ixrt/README.md#fp16) | -| ResNeXt101_64x4d | FP16 | [✅](models/cv/classification/resnext101_64x4d/igie/README.md#fp16) | | -| ResNeXt101_32x8d | FP16 | [✅](models/cv/classification/resnext101_32x8d/igie/README.md#fp16) | | -| SEResNet50 | FP16 | [✅](models/cv/classification/se_resnet50/igie/README.md#fp16) | | -| ShuffleNetV1 | FP16 | | [✅](models/cv/classification/shufflenet_v1/ixrt/README.md#fp16) | -| ShuffleNetV2_x0_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x0_5/igie/README.md#fp16) | | -| ShuffleNetV2_x1_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_0/igie/README.md#fp16) | | -| ShuffleNetV2_x1_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_5/igie/README.md#fp16) | | -| ShuffleNetV2_x2_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x2_0/igie/README.md#fp16) | | -| SqueezeNet 1.0 | FP16 | [✅](models/cv/classification/squeezenet_v1_0/igie/README.md#fp16) | [✅](models/cv/classification/squeezenet_v1_0/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/squeezenet_v1_0/ixrt/README.md#int8) | -| SqueezeNet 1.1 | FP16 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt/README.md#int8) | -| SVT Base | FP16 | [✅](models/cv/classification/svt_base/igie/README.md#fp16) | | -| Swin Transformer | FP16 | [✅](models/cv/classification/swin_transformer/igie/README.md#fp16) | | -| Swin Transformer Large | FP16 | | [✅](models/cv/classification/swin_transformer_large/ixrt/README.md) | -| VGG11 | FP16 | [✅](models/cv/classification/vgg11/igie/README.md#fp16) | | -| VGG16 | FP16 | [✅](models/cv/classification/vgg16/igie/README.md#fp16) | [✅](models/cv/classification/vgg16/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/vgg16/igie/README.md#int8) | | -| Wide ResNet50 | FP16 | [✅](models/cv/classification/wide_resnet50/igie/README.md#fp16) | [✅](models/cv/classification/wide_resnet50/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/wide_resnet50/igie/README.md#int8) | [✅](models/cv/classification/wide_resnet50/ixrt/README.md#int8) | -| Wide ResNet101 | FP16 | [✅](models/cv/classification/wide_resnet101/igie/README.md#fp16) | | - -### Detection - -| Model | Prec. | IGIE | IxRT | -|------------|-------|---------------------------------------------------------|---------------------------------------------------------| -| ATSS | FP16 | [✅](models/cv/detection/atss/igie/README.md#fp16) | | -| CenterNet | FP16 | [✅](models/cv/detection/centernet/igie/README.md#fp16) | [✅](models/cv/detection/centernet/ixrt/README.md#fp16) | -| DETR | FP16 | | [✅](models/cv/detection/detr/ixrt/README.md#fp16) | -| FCOS | FP16 | [✅](models/cv/detection/fcos/igie/README.md#fp16) | [✅](models/cv/detection/fcos/ixrt/README.md#fp16) | -| FoveaBox | FP16 | [✅](models/cv/detection/foveabox/igie/README.md#fp16) | [✅](models/cv/detection/foveabox/ixrt/README.md#fp16) | -| FSAF | FP16 | [✅](models/cv/detection/fsaf/igie/README.md#fp16) | [✅](models/cv/detection/fsaf/ixrt/README.md#fp16) | -| HRNet | FP16 | [✅](models/cv/detection/hrnet/igie/README.md#fp16) | [✅](models/cv/detection/hrnet/ixrt/README.md#fp16) | -| PAA | FP16 | [✅](models/cv/detection/paa/igie/README.md#fp16) | | -| RetinaFace | FP16 | [✅](models/cv/detection/retinaface/igie/README.md#fp16) | [✅](models/cv/detection/retinaface/ixrt/README.md#fp16) | -| RetinaNet | FP16 | [✅](models/cv/detection/retinanet/igie/README.md#fp16) | | -| RTMDet | FP16 | [✅](models/cv/detection/rtmdet/igie/README.md#fp16) | | -| SABL | FP16 | [✅](models/cv/detection/sabl/igie/README.md#fp16) | | -| YOLOv3 | FP16 | [✅](models/cv/detection/yolov3/igie/README.md#fp16) | [✅](models/cv/detection/yolov3/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov3/igie/README.md#int8) | [✅](models/cv/detection/yolov3/ixrt/README.md#int8) | -| YOLOv4 | FP16 | [✅](models/cv/detection/yolov4/igie/README.md#fp16) | [✅](models/cv/detection/yolov4/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov4/igie/README.md#int816) | [✅](models/cv/detection/yolov4/ixrt/README.md#int816) | -| YOLOv5 | FP16 | [✅](models/cv/detection/yolov5/igie/README.md#fp16) | [✅](models/cv/detection/yolov5/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov5/igie/README.md#int8) | [✅](models/cv/detection/yolov5/ixrt/README.md#int8) | -| YOLOv5s | FP16 | | [✅](models/cv/detection/yolov5s/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/detection/yolov5s/ixrt/README.md#int8) | -| YOLOv6 | FP16 | [✅](models/cv/detection/yolov6/igie/README.md#fp16) | [✅](models/cv/detection/yolov6/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/detection/yolov6/ixrt/README.md#int8) | -| YOLOv7 | FP16 | [✅](models/cv/detection/yolov7/igie/README.md#fp16) | [✅](models/cv/detection/yolov7/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov7/igie/README.md#int8) | [✅](models/cv/detection/yolov7/ixrt/README.md#int8) | -| YOLOv8 | FP16 | [✅](models/cv/detection/yolov8/igie/README.md#fp16) | [✅](models/cv/detection/yolov8/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov8/igie/README.md#int8) | [✅](models/cv/detection/yolov8/ixrt/README.md#int8) | -| YOLOv9 | FP16 | [✅](models/cv/detection/yolov9/igie/README.md#fp16) | | -| YOLOv10 | FP16 | [✅](models/cv/detection/yolov10/igie/README.md#fp16) | | -| YOLOv11 | FP16 | [✅](models/cv/detection/yolov11/igie/README.md#fp16) | | -| YOLOX | FP16 | [✅](models/cv/detection/yolox/igie/README.md#fp16) | [✅](models/cv/detection/yolox/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolox/igie/README.md#int8) | [✅](models/cv/detection/yolox/ixrt/README.md#int8) | +| Model | Prec. | IGIE | IxRT | +|------------------------|-------|--------------------------------------------------------|-----------------------------------------------------------| +| AlexNet | FP16 | [✅](models/cv/classification/alexnet/igie) | [✅](models/cv/classification/alexnet/ixrt) | +| | INT8 | [✅](models/cv/classification/alexnet/igie) | [✅](models/cv/classification/alexnet/ixrt) | +| CLIP | FP16 | [✅](models/cv/classification/clip/igie) | | +| Conformer-B | FP16 | [✅](models/cv/classification/conformer_base/igie) | | +| ConvNeXt-Base | FP16 | [✅](models/cv/classification/convnext_base/igie) | [✅](models/cv/classification/convnext_base/ixrt) | +| ConvNext-S | FP16 | [✅](models/cv/classification/convnext_s/igie) | | +| ConvNeXt-Small | FP16 | [✅](models/cv/classification/convnext_small/igie) | [✅](models/cv/classification/convnext_small/ixrt) | +| CSPDarkNet53 | FP16 | [✅](models/cv/classification/cspdarknet53/igie) | [✅](models/cv/classification/cspdarknet53/ixrt) | +| | INT8 | | [✅](models/cv/classification/cspdarknet53/ixrt) | +| CSPResNet50 | FP16 | [✅](models/cv/classification/cspresnet50/igie) | [✅](models/cv/classification/cspresnet50/ixrt) | +| | INT8 | | [✅](models/cv/classification/cspresnet50/ixrt) | +| DeiT-tiny | FP16 | [✅](models/cv/classification/deit_tiny/igie) | [✅](models/cv/classification/deit_tiny/ixrt) | +| DenseNet121 | FP16 | [✅](models/cv/classification/densenet121/igie) | [✅](models/cv/classification/densenet121/ixrt) | +| DenseNet161 | FP16 | [✅](models/cv/classification/densenet161/igie) | [✅](models/cv/classification/densenet161/ixrt) | +| DenseNet169 | FP16 | [✅](models/cv/classification/densenet169/igie) | [✅](models/cv/classification/densenet169/ixrt) | +| DenseNet201 | FP16 | [✅](models/cv/classification/densenet201/igie) | [✅](models/cv/classification/densenet201/ixrt) | +| EfficientNet-B0 | FP16 | [✅](models/cv/classification/efficientnet_b0/igie) | [✅](models/cv/classification/efficientnet_b0/ixrt) | +| | INT8 | | [✅](models/cv/classification/efficientnet_b0/ixrt) | +| EfficientNet-B1 | FP16 | [✅](models/cv/classification/efficientnet_b1/igie) | [✅](models/cv/classification/efficientnet_b1/ixrt) | +| | INT8 | | [✅](models/cv/classification/efficientnet_b1/ixrt) | +| EfficientNet-B2 | FP16 | [✅](models/cv/classification/efficientnet_b2/igie) | [✅](models/cv/classification/efficientnet_b2/ixrt) | +| EfficientNet-B3 | FP16 | [✅](models/cv/classification/efficientnet_b3/igie) | [✅](models/cv/classification/efficientnet_b3/ixrt) | +| EfficientNet-B4 | FP16 | [✅](models/cv/classification/efficientnet_b4/igie) | | +| EfficientNetV2 | FP16 | [✅](models/cv/classification/efficientnet_v2/igie) | [✅](models/cv/classification/efficientnet_v2/ixrt) | +| | INT8 | | [✅](models/cv/classification/efficientnet_v2/ixrt) | +| EfficientNetv2_rw_t | FP16 | [✅](models/cv/classification/efficientnetv2_rw_t/igie) | [✅](models/cv/classification/efficientnetv2_rw_t/ixrt) | +| EfficientNetv2_s | FP16 | [✅](models/cv/classification/efficientnet_v2_s/igie) | [✅](models/cv/classification/efficientnet_v2_s/ixrt) | +| GoogLeNet | FP16 | [✅](models/cv/classification/googlenet/igie) | [✅](models/cv/classification/googlenet/ixrt) | +| | INT8 | [✅](models/cv/classification/googlenet/igie) | [✅](models/cv/classification/googlenet/ixrt) | +| HRNet-W18 | FP16 | [✅](models/cv/classification/hrnet_w18/igie) | [✅](models/cv/classification/hrnet_w18/ixrt) | +| | INT8 | | [✅](models/cv/classification/hrnet_w18/ixrt) | +| InceptionV3 | FP16 | [✅](models/cv/classification/inception_v3/igie) | [✅](models/cv/classification/inception_v3/ixrt) | +| | INT8 | [✅](models/cv/classification/inception_v3/igie) | [✅](models/cv/classification/inception_v3/ixrt) | +| Inception_ResNet_V2 | FP16 | | [✅](models/cv/classification/inceptionresnetv2/ixrt) | +| | INT8 | | [✅](models/cv/classification/inceptionresnetv2/ixrt) | +| Mixer_B | FP16 | [✅](models/cv/classification/mlp_mixer_base/igie) | | +| MNASNet0_5 | FP16 | [✅](models/cv/classification/mnasnet0_5/igie) | | +| MNASNet0_75 | FP16 | [✅](models/cv/classification/mnasnet0_75/igie) | | +| MobileNetV2 | FP16 | [✅](models/cv/classification/mobilenet_v2/igie) | [✅](models/cv/classification/mobilenet_v2/ixrt) | +| | INT8 | [✅](models/cv/classification/mobilenet_v2/igie) | [✅](models/cv/classification/mobilenet_v2/ixrt) | +| MobileNetV3_Large | FP16 | [✅](models/cv/classification/mobilenet_v3_large/igie) | | +| MobileNetV3_Small | FP16 | [✅](models/cv/classification/mobilenet_v3/igie) | [✅](models/cv/classification/mobilenet_v3/ixrt) | +| MViTv2_base | FP16 | [✅](models/cv/classification/mvitv2_base/igie) | | +| RegNet_x_16gf | FP16 | [✅](models/cv/classification/regnet_x_16gf/igie) | | +| RegNet_x_1_6gf | FP16 | [✅](models/cv/classification/regnet_x_1_6gf/igie) | | +| RegNet_y_1_6gf | FP16 | [✅](models/cv/classification/regnet_y_1_6gf/igie) | | +| RepVGG | FP16 | [✅](models/cv/classification/repvgg/igie) | [✅](models/cv/classification/repvgg/ixrt) | +| Res2Net50 | FP16 | [✅](models/cv/classification/res2net50/igie) | [✅](models/cv/classification/res2net50/ixrt) | +| | INT8 | | [✅](models/cv/classification/res2net50/ixrt) | +| ResNeSt50 | FP16 | [✅](models/cv/classification/resnest50/igie) | | +| ResNet101 | FP16 | [✅](models/cv/classification/resnet101/igie) | [✅](models/cv/classification/resnet101/ixrt) | +| | INT8 | [✅](models/cv/classification/resnet101/igie) | [✅](models/cv/classification/resnet101/ixrt) | +| ResNet152 | FP16 | [✅](models/cv/classification/resnet152/igie) | | +| | INT8 | [✅](models/cv/classification/resnet152/igie) | | +| ResNet18 | FP16 | [✅](models/cv/classification/resnet18/igie) | [✅](models/cv/classification/resnet18/ixrt) | +| | INT8 | [✅](models/cv/classification/resnet18/igie) | [✅](models/cv/classification/resnet18/ixrt) | +| ResNet34 | FP16 | | [✅](models/cv/classification/resnet34/ixrt) | +| | INT8 | | [✅](models/cv/classification/resnet34/ixrt) | +| ResNet50 | FP16 | [✅](models/cv/classification/resnet50/igie) | [✅](models/cv/classification/resnet50/ixrt) | +| | INT8 | [✅](models/cv/classification/resnet50/igie) | | +| ResNetV1D50 | FP16 | [✅](models/cv/classification/resnetv1d50/igie) | [✅](models/cv/classification/resnetv1d50/ixrt) | +| | INT8 | | [✅](models/cv/classification/resnetv1d50/ixrt) | +| ResNeXt50_32x4d | FP16 | [✅](models/cv/classification/resnext50_32x4d/igie) | [✅](models/cv/classification/resnext50_32x4d/ixrt) | +| ResNeXt101_64x4d | FP16 | [✅](models/cv/classification/resnext101_64x4d/igie) | | +| ResNeXt101_32x8d | FP16 | [✅](models/cv/classification/resnext101_32x8d/igie) | | +| SEResNet50 | FP16 | [✅](models/cv/classification/se_resnet50/igie) | | +| ShuffleNetV1 | FP16 | | [✅](models/cv/classification/shufflenet_v1/ixrt) | +| ShuffleNetV2_x0_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x0_5/igie) | | +| ShuffleNetV2_x1_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_0/igie) | | +| ShuffleNetV2_x1_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_5/igie) | | +| ShuffleNetV2_x2_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x2_0/igie) | | +| SqueezeNet 1.0 | FP16 | [✅](models/cv/classification/squeezenet_v1_0/igie) | [✅](models/cv/classification/squeezenet_v1_0/ixrt) | +| | INT8 | | [✅](models/cv/classification/squeezenet_v1_0/ixrt) | +| SqueezeNet 1.1 | FP16 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt) | +| | INT8 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt) | +| SVT Base | FP16 | [✅](models/cv/classification/svt_base/igie) | | +| Swin Transformer | FP16 | [✅](models/cv/classification/swin_transformer/igie) | | +| Swin Transformer Large | FP16 | | [✅](models/cv/classification/swin_transformer_large/ixrt) | +| VGG11 | FP16 | [✅](models/cv/classification/vgg11/igie) | | +| VGG16 | FP16 | [✅](models/cv/classification/vgg16/igie) | [✅](models/cv/classification/vgg16/ixrt) | +| | INT8 | [✅](models/cv/classification/vgg16/igie) | | +| Wide ResNet50 | FP16 | [✅](models/cv/classification/wide_resnet50/igie) | [✅](models/cv/classification/wide_resnet50/ixrt) | +| | INT8 | [✅](models/cv/classification/wide_resnet50/igie) | [✅](models/cv/classification/wide_resnet50/ixrt) | +| Wide ResNet101 | FP16 | [✅](models/cv/classification/wide_resnet101/igie) | | + +### Object Detection + +| Model | Prec. | IGIE | IxRT | +|------------|-------|------------------------------------------|------------------------------------------| +| ATSS | FP16 | [✅](models/cv/detection/atss/igie) | | +| CenterNet | FP16 | [✅](models/cv/detection/centernet/igie) | [✅](models/cv/detection/centernet/ixrt) | +| DETR | FP16 | | [✅](models/cv/detection/detr/ixrt) | +| FCOS | FP16 | [✅](models/cv/detection/fcos/igie) | [✅](models/cv/detection/fcos/ixrt) | +| FoveaBox | FP16 | [✅](models/cv/detection/foveabox/igie) | [✅](models/cv/detection/foveabox/ixrt) | +| FSAF | FP16 | [✅](models/cv/detection/fsaf/igie) | [✅](models/cv/detection/fsaf/ixrt) | +| HRNet | FP16 | [✅](models/cv/detection/hrnet/igie) | [✅](models/cv/detection/hrnet/ixrt) | +| PAA | FP16 | [✅](models/cv/detection/paa/igie) | | +| RetinaFace | FP16 | [✅](models/cv/detection/retinaface/igie) | [✅](models/cv/detection/retinaface/ixrt) | +| RetinaNet | FP16 | [✅](models/cv/detection/retinanet/igie) | | +| RTMDet | FP16 | [✅](models/cv/detection/rtmdet/igie) | | +| SABL | FP16 | [✅](models/cv/detection/sabl/igie) | | +| YOLOv3 | FP16 | [✅](models/cv/detection/yolov3/igie) | [✅](models/cv/detection/yolov3/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov3/igie) | [✅](models/cv/detection/yolov3/ixrt) | +| YOLOv4 | FP16 | [✅](models/cv/detection/yolov4/igie) | [✅](models/cv/detection/yolov4/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov4/igie16) | [✅](models/cv/detection/yolov4/ixrt16) | +| YOLOv5 | FP16 | [✅](models/cv/detection/yolov5/igie) | [✅](models/cv/detection/yolov5/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov5/igie) | [✅](models/cv/detection/yolov5/ixrt) | +| YOLOv5s | FP16 | | [✅](models/cv/detection/yolov5s/ixrt) | +| | INT8 | | [✅](models/cv/detection/yolov5s/ixrt) | +| YOLOv6 | FP16 | [✅](models/cv/detection/yolov6/igie) | [✅](models/cv/detection/yolov6/ixrt) | +| | INT8 | | [✅](models/cv/detection/yolov6/ixrt) | +| YOLOv7 | FP16 | [✅](models/cv/detection/yolov7/igie) | [✅](models/cv/detection/yolov7/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov7/igie) | [✅](models/cv/detection/yolov7/ixrt) | +| YOLOv8 | FP16 | [✅](models/cv/detection/yolov8/igie) | [✅](models/cv/detection/yolov8/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov8/igie) | [✅](models/cv/detection/yolov8/ixrt) | +| YOLOv9 | FP16 | [✅](models/cv/detection/yolov9/igie) | | +| YOLOv10 | FP16 | [✅](models/cv/detection/yolov10/igie) | | +| YOLOv11 | FP16 | [✅](models/cv/detection/yolov11/igie) | | +| YOLOX | FP16 | [✅](models/cv/detection/yolox/igie) | [✅](models/cv/detection/yolox/ixrt) | +| | INT8 | [✅](models/cv/detection/yolox/igie) | [✅](models/cv/detection/yolox/ixrt) | ### Face Recognition -| Model | Prec. | IGIE | IxRT | -|---------|-------|------|-------------------------------------------------| -| FaceNet | FP16 | | [✅](models/cv/face/facenet/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/face/facenet/ixrt/README.md#int8) | +| Model | Prec. | IGIE | IxRT | +|---------|-------|------|----------------------------------| +| FaceNet | FP16 | | [✅](models/cv/face/facenet/ixrt) | +| | INT8 | | [✅](models/cv/face/facenet/ixrt) | ### OCR -| Model | Prec. | IGIE | -|---------------|-----------|------------------------------------------------------| -| Kie_layoutXLM | FP16 | [✅](models/cv/ocr/kie_layoutxlm/igie/README.md#fp16) | -| SVTR | FP16 | [✅](models/cv/ocr/svtr/igie/README.md#fp16) | +| Model | Prec. | IGIE | +|---------------|-------|---------------------------------------| +| Kie_layoutXLM | FP16 | [✅](models/cv/ocr/kie_layoutxlm/igie) | +| SVTR | FP16 | [✅](models/cv/ocr/svtr/igie) | ### Pose Estimation -| Model | Prec. | IGIE | IxRT | -|----------------------|-------|--------------------------------------------------------------|-------------------------------------------------------------------------| -| HRNetPose | FP16 | [✅](models/cv/pose_estimation/hrnetpose/igie/README.md#fp16) | | -| Lightweight OpenPose | FP16 | | [✅](models/cv/pose_estimation/lightweight_openpose/ixrt/README.md#fp16) | -| RTMPose | FP16 | [✅](models/cv/pose_estimation/rtmpose/igie/README.md#fp16) | [✅](models/cv/pose_estimation/rtmpose/ixrt/README.md#fp16) | - -### Recommendation Systems - -| Model | Prec. | IGIE | IxRT | -|-------------|-------|------|------------------------------------------------------------------------| -| Wide & Deep | FP16 | | [✅](models/recommendation/ctr-prediction/widedeep/ixrt/README.md#fp16) | +| Model | Prec. | IGIE | IxRT | +|----------------------|-------|-----------------------------------------------|----------------------------------------------------------| +| HRNetPose | FP16 | [✅](models/cv/pose_estimation/hrnetpose/igie) | | +| Lightweight OpenPose | FP16 | | [✅](models/cv/pose_estimation/lightweight_openpose/ixrt) | +| RTMPose | FP16 | [✅](models/cv/pose_estimation/rtmpose/igie) | [✅](models/cv/pose_estimation/rtmpose/ixrt) | ### Segmentation -| Model | Prec. | IGIE | IxRT | -|------------|-------|------|-----------------------------------------------------------| -| Mask R-CNN | FP16 | | [✅](models/cv/segmentation/mask_rcnn/ixrt/README.md#fp16) | -| SOLOv1 | FP16 | | [✅](models/cv/segmentation/solov1/ixrt/README.md#fp16) | +| Model | Prec. | IGIE | IxRT | +|------------|-------|------|--------------------------------------------| +| Mask R-CNN | FP16 | | [✅](models/cv/segmentation/mask_rcnn/ixrt) | +| SOLOv1 | FP16 | | [✅](models/cv/segmentation/solov1/ixrt) | ### Trace -| Model | Prec. | IGIE | IxRT | -|---------------------|-------|---------------------------------------------------|------| -| FastReID | FP16 | [✅](models/cv/trace/fastreid/igie/README.md#fp16) | | -| DeepSort | FP16 | [✅](models/cv/trace/deepsort/igie/README.md#fp16) | | -| | INT8 | [✅](models/cv/trace/deepsort/igie/README.md#int8) | | -| RepNet-Vehicle-ReID | FP16 | [✅](models/cv/trace/repnet/igie/README.md#fp16) | | - -## LLM (Large Language Model) - -| Model | vLLM | TRT-LLM | TGI | -|-------------------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------|-------------------------------------------------------------------------------------| -| Baichuan2-7B | [✅](models/nlp/large_language_model/baichuan2-7b/vllm/README.md) | | | -| ChatGLM-3-6B | [✅](models/nlp/large_language_model/chatglm3-6b/vllm/README.md) | | | -| ChatGLM-3-6B-32K | [✅](models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md) | | | -| DeepSeek-R1-Distill-Llama-8B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Llama-70B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-1.5B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-7B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-14B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-32B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md) | | | -| Llama2-7B | [✅](models/nlp/large_language_model/llama2-7b/vllm/README.md) | [✅](models/nlp/large_language_model/llama2-7b/trtllm/README.md) | | -| Llama2-13B | | [✅](models/nlp/large_language_model/llama2-13b/trtllm/README.md) | | -| Llama2-70B | | [✅](models/nlp/large_language_model/llama2-70b/trtllm/README.md) | | -| Llama3-70B | [✅](models/nlp/large_language_model/llama3-70b/vllm/README.md) | | | -| Qwen-7B | [✅](models/nlp/large_language_model/qwen-7b/vllm/README.md) | | | -| Qwen1.5-7B | [✅](models/nlp/large_language_model/qwen1.5-7b/vllm/README.md) | | [✅](models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md) | -| Qwen1.5-14B | [✅](models/nlp/large_language_model/qwen1.5-14b/vllm/README.md) | | | -| Qwen1.5-32B Chat | [✅](models/nlp/large_language_model/qwen1.5-32b/vllm/README.md) | | | -| Qwen1.5-72B | [✅](models/nlp/large_language_model/qwen1.5-72b/vllm/README.md) | | | -| Qwen2-7B Instruct | [✅](models/nlp/large_language_model/qwen2-7b/vllm/README.md) | | | -| Qwen2-72B Instruct | [✅](models/nlp/large_language_model/qwen2-72b/vllm/README.md) | | | -| StableLM2-1.6B | [✅](models/nlp/large_language_model/stablelm/vllm/README.md) | | | +| Model | Prec. | IGIE | IxRT | +|---------------------|-------|------------------------------------|------| +| FastReID | FP16 | [✅](models/cv/trace/fastreid/igie) | | +| DeepSort | FP16 | [✅](models/cv/trace/deepsort/igie) | | +| | INT8 | [✅](models/cv/trace/deepsort/igie) | | +| RepNet-Vehicle-ReID | FP16 | [✅](models/cv/trace/repnet/igie) | | ## Multimodal -| Model | vLLM | IxFormer | -|---------------------|-----------------------------------------------------------------------------------------|------------------------------------------------------------------------------| -| Chameleon-7B | [✅](models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md) | | -| CLIP | | [✅](models/multimodal/vision_language_understanding/clip/ixformer/README.md) | -| Fuyu-8B | [✅](models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md) | | -| InternVL2-4B | [✅](models/multimodal/vision_language_understanding/intern_vl/vllm/README.md) | | -| LLaVA | [✅](models/multimodal/vision_language_understanding/llava/vllm/README.md) | | -| LLaVA-Next-Video-7B | [✅](models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md) | | -| MiniCPM V2 | [✅](models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md) | | +| Model | vLLM | IxFormer | +|---------------------|-----------------------------------------------------------------------|------------------------------------------------------------| +| Chameleon-7B | [✅](models/multimodal/vision_language_model/chameleon_7b/vllm) | | +| CLIP | | [✅](models/multimodal/vision_language_model/clip/ixformer) | +| Fuyu-8B | [✅](models/multimodal/vision_language_model/fuyu_8b/vllm) | | +| InternVL2-4B | [✅](models/multimodal/vision_language_model/intern_vl/vllm) | | +| LLaVA | [✅](models/multimodal/vision_language_model/llava/vllm) | | +| LLaVA-Next-Video-7B | [✅](models/multimodal/vision_language_model/llava_next_video_7b/vllm) | | +| MiniCPM V2 | [✅](models/multimodal/vision_language_model/minicpm_v_2/vllm) | | ## NLP ### Language Modelling -| Model | Prec. | IGIE | IxRT | -|------------------|-------|---------------------------------------------------------------------|---------------------------------------------------------------------| -| ALBERT | FP16 | | [✅](models/nlp/language_model/albert/ixrt/README.md) | -| BERT Base NER | INT8 | [✅](models/nlp/language_model/bert_base_ner/igie/README.md#int8) | | -| BERT Base SQuAD | FP16 | [✅](models/nlp/language_model/bert_base_squad/igie/README.md#fp16) | [✅](models/nlp/language_model/bert_base_squad/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/nlp/language_model/bert_base_squad/ixrt/README.md#int8) | -| BERT Large SQuAD | FP16 | [✅](models/nlp/language_model/bert_large_squad/igie/README.md#fp16) | [✅](models/nlp/language_model/bert_large_squad/ixrt/README.md#fp16) | -| | INT8 | [✅](models/nlp/language_model/bert_large_squad/igie/README.md#int8) | [✅](models/nlp/language_model/bert_large_squad/ixrt/README.md#int8) | -| DeBERTa | FP16 | | [✅](models/nlp/language_model/deberta/ixrt/README.md) | -| RoBERTa | FP16 | | [✅](models/nlp/language_model/roberta/ixrt/README.md) | -| RoFormer | FP16 | | [✅](models/nlp/language_model/roformer/ixrt/README.md) | -| VideoBERT | FP16 | | [✅](models/nlp/language_model/videobert/ixrt/README.md) | +| Model | Prec. | IGIE | IxRT | +|------------------|-------|------------------------------------------------------|------------------------------------------------------| +| ALBERT | FP16 | | [✅](models/nlp/language_model/albert/ixrt) | +| BERT Base NER | INT8 | [✅](models/nlp/language_model/bert_base_ner/igie) | | +| BERT Base SQuAD | FP16 | [✅](models/nlp/language_model/bert_base_squad/igie) | [✅](models/nlp/language_model/bert_base_squad/ixrt) | +| | INT8 | | [✅](models/nlp/language_model/bert_base_squad/ixrt) | +| BERT Large SQuAD | FP16 | [✅](models/nlp/language_model/bert_large_squad/igie) | [✅](models/nlp/language_model/bert_large_squad/ixrt) | +| | INT8 | [✅](models/nlp/language_model/bert_large_squad/igie) | [✅](models/nlp/language_model/bert_large_squad/ixrt) | +| DeBERTa | FP16 | | [✅](models/nlp/language_model/deberta/ixrt) | +| RoBERTa | FP16 | | [✅](models/nlp/language_model/roberta/ixrt) | +| RoFormer | FP16 | | [✅](models/nlp/language_model/roformer/ixrt) | +| VideoBERT | FP16 | | [✅](models/nlp/language_model/videobert/ixrt) | ## Speech ### Speech Recognition -| Model | Prec. | IGIE | IxRT | -|-----------------|-------|---------------------------------------------------------------------|----------------------------------------------------------------------| -| Conformer | FP16 | [✅](models/speech/speech_recognition/conformer/igie/README.md#fp16) | [✅](models/speech/speech_recognition/conformer/ixrt/README.md#fp16) | -| Transformer ASR | FP16 | | [✅](models/speech/speech_recognition/transformer_asr/ixrt/README.md) | +| Model | Prec. | IGIE | IxRT | +|-----------------|-------|-----------------------------------------------------|-----------------------------------------------------------| +| Conformer | FP16 | [✅](models/audio/speech_recognition/conformer/igie) | [✅](models/audio/speech_recognition/conformer/ixrt) | +| Transformer ASR | FP16 | | [✅](models/audio/speech_recognition/transformer_asr/ixrt) | + +## Others + +### Recommendation Systems + +| Model | Prec. | IGIE | IxRT | +|-------------|-------|------|---------------------------------------------------------| +| Wide & Deep | FP16 | | [✅](models/recommendation/ctr-prediction/widedeep/ixrt) | --- diff --git a/docs/README_TEMPLATE.md b/docs/README_TEMPLATE.md index 14661a10a5043b6274eec5009caa36e1f43a2092..345a38ed6af8de27372b6e6307de3cfe468f3c89 100644 --- a/docs/README_TEMPLATE.md +++ b/docs/README_TEMPLATE.md @@ -1,6 +1,7 @@ # MODEL_NAME ## Description + A brief introduction about this model. ## Setup @@ -20,6 +21,7 @@ bash test_fp16.sh ``` ### INT8 + ```bash bash test_int8.sh ``` @@ -31,4 +33,5 @@ Model | BatchSize | Precision | FPS | ACC MODEL_NAME | | | | ## Referenece (remove if not necessary) + - [refer-page-name](https://refer-links) diff --git a/models/speech/speech_recognition/conformer/igie/README.md b/models/audio/speech_recognition/conformer/igie/README.md similarity index 75% rename from models/speech/speech_recognition/conformer/igie/README.md rename to models/audio/speech_recognition/conformer/igie/README.md index 28cb7f7bb847698b9105ad4033b14c08a106c981..47bafe826dd1179629c058217bbe39af672c3cfd 100644 --- a/models/speech/speech_recognition/conformer/igie/README.md +++ b/models/audio/speech_recognition/conformer/igie/README.md @@ -2,7 +2,10 @@ ## Description -Conformer is a speech recognition model proposed by Google in 2020. It combines the advantages of CNN and Transformer. CNN efficiently extracts local features, while Transformer is more effective in capturing long sequence dependencies. Conformer applies convolution to the Encoder layer of Transformer, enhancing the performance of Transformer in the ASR (Automatic Speech Recognition) domain. +Conformer is a speech recognition model proposed by Google in 2020. It combines the advantages of CNN and Transformer. +CNN efficiently extracts local features, while Transformer is more effective in capturing long sequence dependencies. +Conformer applies convolution to the Encoder layer of Transformer, enhancing the performance of Transformer in the ASR +(Automatic Speech Recognition) domain. ## Setup @@ -62,6 +65,6 @@ bash scripts/infer_conformer_fp16_performance.sh ## Results -Model |BatchSize |Precision |FPS |ACC | ------------|-----------|----------|----------|----------| -Conformer | 32 | FP16 | 1940.759 | 95.29 | +| Model | BatchSize | Precision | FPS | ACC | +|-----------|-----------|-----------|----------|-------| +| Conformer | 32 | FP16 | 1940.759 | 95.29 | diff --git a/models/speech/speech_recognition/conformer/igie/alter_onnx.py b/models/audio/speech_recognition/conformer/igie/alter_onnx.py similarity index 100% rename from models/speech/speech_recognition/conformer/igie/alter_onnx.py rename to 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a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/kaldi-pipebuf.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/kaldi-pipebuf.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/kaldi-pipebuf.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/kaldi-pipebuf.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/parse-options.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/parse-options.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/parse-options.cc rename to 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similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/stl-utils.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/stl-utils.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/text-utils.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/text-utils.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/text-utils.cc rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/text-utils.cc diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/kaldi/util/text-utils.h 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a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/json.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/json.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/json.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/json.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/log.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/log.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/log.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/log.h diff --git 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a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.cc rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.cc diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/utils.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/VoiceRectView.java b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/VoiceRectView.java similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/VoiceRectView.java rename to 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models/speech/speech_recognition/conformer/ixrt/wenet/processor.py rename to models/audio/speech_recognition/conformer/ixrt/wenet/processor.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/README.md b/models/audio/speech_recognition/transformer_asr/ixrt/README.md similarity index 86% rename from models/speech/speech_recognition/transformer_asr/ixrt/README.md rename to models/audio/speech_recognition/transformer_asr/ixrt/README.md index 9d809a7dc8c2216faf0a0791942aa4d93eebce32..601362a2c8d47c1d0cf6f14357918b412a4c217a 100644 --- a/models/speech/speech_recognition/transformer_asr/ixrt/README.md +++ b/models/audio/speech_recognition/transformer_asr/ixrt/README.md @@ -2,7 +2,9 @@ ## Description -Beam search allows us to exert control over the output of text generation. This is useful because we sometimes know exactly what we want inside the output. For example, in a Neural Machine Translation task, we might know which words must be included in the final translation with a dictionary lookup. +Beam search allows us to exert control over the output of text generation. This is useful because we sometimes know +exactly what we want inside the output. For example, in a Neural Machine Translation task, we might know which words +must be included in the final translation with a dictionary lookup. ## Setup @@ -79,5 +81,5 @@ python3 inference.py hparams/train_ASR_transformer.yaml --data_folder=/home/data ## Results | Model | BatchSize | Precision | QPS | CER | -| --------------- | --------- | --------- | ----- | ---- | +|-----------------|-----------|-----------|-------|------| | Transformer ASR | 32 | FP16 | 15.64 | 5.95 | diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/aishell_prepare.py b/models/audio/speech_recognition/transformer_asr/ixrt/aishell_prepare.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/aishell_prepare.py rename to models/audio/speech_recognition/transformer_asr/ixrt/aishell_prepare.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/beam_search.py b/models/audio/speech_recognition/transformer_asr/ixrt/beam_search.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/beam_search.py rename to models/audio/speech_recognition/transformer_asr/ixrt/beam_search.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/build.sh b/models/audio/speech_recognition/transformer_asr/ixrt/build.sh similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/build.sh rename to models/audio/speech_recognition/transformer_asr/ixrt/build.sh diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/builder.py b/models/audio/speech_recognition/transformer_asr/ixrt/builder.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/builder.py rename to models/audio/speech_recognition/transformer_asr/ixrt/builder.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/ci/prepare.sh b/models/audio/speech_recognition/transformer_asr/ixrt/ci/prepare.sh similarity index 100% rename from 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a/models/speech/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml b/models/audio/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml rename to models/audio/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/inference.py b/models/audio/speech_recognition/transformer_asr/ixrt/inference.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/inference.py rename to models/audio/speech_recognition/transformer_asr/ixrt/inference.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py b/models/audio/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py rename to models/audio/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/requirements.txt b/models/audio/speech_recognition/transformer_asr/ixrt/requirements.txt similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/requirements.txt rename to models/audio/speech_recognition/transformer_asr/ixrt/requirements.txt diff --git a/models/cv/classification/alexnet/igie/README.md b/models/cv/classification/alexnet/igie/README.md index 4e8418bd882f63fd8000ef76d4d1bba22b606cfa..0720e0ffd3ab813085dc2753f1d85dbd24bde671 100644 --- a/models/cv/classification/alexnet/igie/README.md +++ b/models/cv/classification/alexnet/igie/README.md @@ -2,7 +2,11 @@ ## Description -AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, is a groundbreaking convolutional neural network (CNN) architecture that achieved remarkable success in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC). This neural network comprises eight layers, incorporating five convolutional layers and three fully connected layers. The architecture employs the Rectified Linear Unit (ReLU) activation function to introduce non-linearity, allowing the model to learn complex features from input images. +AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, is a groundbreaking convolutional neural +network (CNN) architecture that achieved remarkable success in the 2012 ImageNet Large Scale Visual Recognition +Challenge (ILSVRC). This neural network comprises eight layers, incorporating five convolutional layers and three fully +connected layers. The architecture employs the Rectified Linear Unit (ReLU) activation function to introduce +non-linearity, allowing the model to learn complex features from input images. ## Setup @@ -50,7 +54,7 @@ bash scripts/infer_alexnet_int8_performance.sh ## Results -Model |BatchSize |Precision |FPS |Top-1(%) |Top-5(%) ---------|-----------|----------|----------|---------|-------- -AlexNet | 32 | FP16 | 20456.16 | 56.53 | 79.05 -AlexNet | 32 | INT8 | 22465.46 | 55.96 | 78.83 +| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) | +|---------|-----------|-----------|----------|----------|----------| +| AlexNet | 32 | FP16 | 20456.16 | 56.53 | 79.05 | +| AlexNet | 32 | INT8 | 22465.46 | 55.96 | 78.83 | diff --git a/models/cv/classification/alexnet/ixrt/README.md b/models/cv/classification/alexnet/ixrt/README.md index 65fa53efbc1115f3c9bb26c820afa1337d78a561..a111e4060d0b219ddcbf6e87ced599e4955d27df 100644 --- a/models/cv/classification/alexnet/ixrt/README.md +++ b/models/cv/classification/alexnet/ixrt/README.md @@ -2,7 +2,8 @@ ## Description -AlexNet is a classic convolutional neural network architecture. It consists of convolutions, max pooling and dense layers as the basic building blocks. +AlexNet is a classic convolutional neural network architecture. It consists of convolutions, max pooling and dense +layers as the basic building blocks. ## Setup @@ -13,7 +14,7 @@ AlexNet is a classic convolutional neural network architecture. It consists of c ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` @@ -61,7 +62,7 @@ bash scripts/infer_alexnet_int8_performance.sh ## Results -Model |BatchSize |Precision |FPS |Top-1(%) |Top-5(%) ---------|-----------|----------|----------|----------|-------- -AlexNet | 32 | FP16 | 17644.90 | 56.54 | 79.08 -AlexNet | 32 | INT8 | 18276.83 | 55.37 | 79.04 +| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) | +|---------|-----------|-----------|----------|----------|----------| +| AlexNet | 32 | FP16 | 17644.90 | 56.54 | 79.08 | +| AlexNet | 32 | INT8 | 18276.83 | 55.37 | 79.04 | diff --git a/models/cv/classification/convnext_base/ixrt/README.md b/models/cv/classification/convnext_base/ixrt/README.md index 62bfb23c5f1280728eea1f0e0ca843441e6ad64f..0e4da2e6bd8188db4833bfd88f2608b202982991 100644 --- a/models/cv/classification/convnext_base/ixrt/README.md +++ b/models/cv/classification/convnext_base/ixrt/README.md @@ -13,7 +13,7 @@ The ConvNeXt Base model represents a significant stride in the evolution of conv ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/convnext_s/igie/README.md b/models/cv/classification/convnext_s/igie/README.md index 55072b2aa3106eb15304c9a961a1feb9fbe339ff..9d133a66fc0ca1eef042495cfa1254d3f670a58c 100644 --- a/models/cv/classification/convnext_s/igie/README.md +++ b/models/cv/classification/convnext_s/igie/README.md @@ -13,7 +13,7 @@ ConvNeXt-S is a small-sized model in the ConvNeXt family, designed to balance pe ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/convnext_small/ixrt/README.md b/models/cv/classification/convnext_small/ixrt/README.md index 2e9f8521adfbfdbe650607a3810ba00b52c43841..70d1e0a8ba61705bc4b5af15ffd3bd6804f728b1 100644 --- a/models/cv/classification/convnext_small/ixrt/README.md +++ b/models/cv/classification/convnext_small/ixrt/README.md @@ -13,7 +13,7 @@ The ConvNeXt Small model represents a significant stride in the evolution of con ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspdarknet53/igie/README.md b/models/cv/classification/cspdarknet53/igie/README.md index 889b61b416e5115b6e68cb8577c717c5278695c1..ca15f15aa820c37d196a48199d04709ebe928647 100644 --- a/models/cv/classification/cspdarknet53/igie/README.md +++ b/models/cv/classification/cspdarknet53/igie/README.md @@ -13,7 +13,7 @@ CSPDarkNet53 is an enhanced convolutional neural network architecture that reduc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspdarknet53/ixrt/README.md b/models/cv/classification/cspdarknet53/ixrt/README.md index b8fc83ae0ebcaf4893f1cfca2dc101b636cb4ed0..40addd41de5c37c861b3944bf04364bd73bbcd5b 100644 --- a/models/cv/classification/cspdarknet53/ixrt/README.md +++ b/models/cv/classification/cspdarknet53/ixrt/README.md @@ -13,7 +13,7 @@ CSPDarkNet53 is an enhanced convolutional neural network architecture that reduc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspresnet50/igie/README.md b/models/cv/classification/cspresnet50/igie/README.md index 8fc072a9bb95e2993249fc02505971ba03a272fc..46341ba62511a13a6dd01205b7ea7c4d2e999139 100644 --- a/models/cv/classification/cspresnet50/igie/README.md +++ b/models/cv/classification/cspresnet50/igie/README.md @@ -13,7 +13,7 @@ CSPResNet50 combines the strengths of ResNet50 and CSPNet (Cross-Stage Partial N ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspresnet50/ixrt/README.md b/models/cv/classification/cspresnet50/ixrt/README.md index a95c9a4c99cf3d1678b2b5da234a456b40151a9f..fa054551f15b187883bfdf049c48202e37e7b638 100644 --- a/models/cv/classification/cspresnet50/ixrt/README.md +++ b/models/cv/classification/cspresnet50/ixrt/README.md @@ -14,7 +14,7 @@ CSPResNet50 is the one of best models. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/deit_tiny/igie/README.md b/models/cv/classification/deit_tiny/igie/README.md index a39dbfd9bc9e95555112d2a482bed62268069509..89cb1aa4720f995757568638d39366e9b4ad313e 100644 --- a/models/cv/classification/deit_tiny/igie/README.md +++ b/models/cv/classification/deit_tiny/igie/README.md @@ -13,7 +13,7 @@ DeiT Tiny is a lightweight vision transformer designed for data-efficient learni ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/deit_tiny/ixrt/README.md b/models/cv/classification/deit_tiny/ixrt/README.md index 15fb025e9badbdb2f303f13a977675638489c90e..42710f0ff58b11534208e7d331895f72f77bcc4f 100644 --- a/models/cv/classification/deit_tiny/ixrt/README.md +++ b/models/cv/classification/deit_tiny/ixrt/README.md @@ -13,7 +13,7 @@ DeiT Tiny is a lightweight vision transformer designed for data-efficient learni ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/densenet121/ixrt/README.md b/models/cv/classification/densenet121/ixrt/README.md index f506c085b2dadc6c4a62164c258bc571d51ac27e..7e2afd493dc8bd5ec6ee9377faffa9ca3b53a9bb 100644 --- a/models/cv/classification/densenet121/ixrt/README.md +++ b/models/cv/classification/densenet121/ixrt/README.md @@ -13,7 +13,7 @@ Dense Convolutional Network (DenseNet), connects each layer to every other layer ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/densenet161/ixrt/README.md b/models/cv/classification/densenet161/ixrt/README.md index 5e225c5e414d99326553cb5517ddeaf8e5eb4d81..58659f827d1e95fea322f779839cfb911de51b32 100644 --- a/models/cv/classification/densenet161/ixrt/README.md +++ b/models/cv/classification/densenet161/ixrt/README.md @@ -13,7 +13,7 @@ DenseNet161 is a convolutional neural network architecture that belongs to the f ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/densenet169/ixrt/README.md b/models/cv/classification/densenet169/ixrt/README.md index 480e9df61520691c764dff53ee2279f3f0d195f7..0e3aee4c481f79324e51fbb5aa9913a9e2f17986 100644 --- a/models/cv/classification/densenet169/ixrt/README.md +++ b/models/cv/classification/densenet169/ixrt/README.md @@ -13,7 +13,7 @@ Dense Convolutional Network (DenseNet), connects each layer to every other layer ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/densenet201/ixrt/README.md b/models/cv/classification/densenet201/ixrt/README.md index 9475535534e4da7c0dc4710d51addbcaaec866d3..e7e2b9ae04ce38cd28fba3310f5a7638fffa4769 100644 --- a/models/cv/classification/densenet201/ixrt/README.md +++ b/models/cv/classification/densenet201/ixrt/README.md @@ -13,7 +13,7 @@ DenseNet201 is a deep convolutional neural network that stands out for its uniqu ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/efficientnet_b0/ixrt/README.md b/models/cv/classification/efficientnet_b0/ixrt/README.md index 84065777ad2f7241c2684d0bbbe119788912a3a3..e7c96a2823a069f719e30fea51e13128d8f5ffee 100644 --- a/models/cv/classification/efficientnet_b0/ixrt/README.md +++ b/models/cv/classification/efficientnet_b0/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B0 is a convolutional neural network architecture that belongs to t ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnet_b1/ixrt/README.md b/models/cv/classification/efficientnet_b1/ixrt/README.md index 282b6440b765402baa6a520b4027219ae03fe41c..a2076bbb06b268856365f058d86eab715e841e59 100644 --- a/models/cv/classification/efficientnet_b1/ixrt/README.md +++ b/models/cv/classification/efficientnet_b1/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B1 is one of the variants in the EfficientNet family of neural netw ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnet_b2/ixrt/README.md b/models/cv/classification/efficientnet_b2/ixrt/README.md index cc22549122bcc9ae4da6502a3d1dc511b82cbfd3..059c80ba683405433fbe063a3c5413949f3450b2 100644 --- a/models/cv/classification/efficientnet_b2/ixrt/README.md +++ b/models/cv/classification/efficientnet_b2/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B2 is a member of the EfficientNet family, a series of convolutiona ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnet_b3/ixrt/README.md b/models/cv/classification/efficientnet_b3/ixrt/README.md index 9963f34fc07adb7214ec00ad4941a058472b9eb0..1860a5e231f4c869a8ff5a8ce4e9773756fb9cc6 100644 --- a/models/cv/classification/efficientnet_b3/ixrt/README.md +++ b/models/cv/classification/efficientnet_b3/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B3 is a member of the EfficientNet family, a series of convolutiona ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/efficientnet_v2/ixrt/README.md b/models/cv/classification/efficientnet_v2/ixrt/README.md index ee9197497d5d996aaa532cfc0961674b856b37e6..c83e812f84deb22aaaa5819bedef2720b44d8637 100755 --- a/models/cv/classification/efficientnet_v2/ixrt/README.md +++ b/models/cv/classification/efficientnet_v2/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNetV2 is an improved version of the EfficientNet architecture proposed ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md b/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md index bc6836d029c2b1b8312e5fd363d1075158dfd910..1e5d56d0d65c9b38a61cc9e5635d722ca76266ff 100644 --- a/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md +++ b/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNetV2_rw_t is an enhanced version of the EfficientNet family of convolu ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install timm diff --git a/models/cv/classification/googlenet/ixrt/README.md b/models/cv/classification/googlenet/ixrt/README.md index fa96b4759f7c863ba0c6d3a59e96c1629f59c690..31170f0d714fa87784db194b5bce63ee37ad962a 100644 --- a/models/cv/classification/googlenet/ixrt/README.md +++ b/models/cv/classification/googlenet/ixrt/README.md @@ -13,7 +13,7 @@ GoogLeNet is a type of convolutional neural network based on the Inception archi ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/hrnet_w18/igie/README.md b/models/cv/classification/hrnet_w18/igie/README.md index bb1aaf23d26d38dd654695ff623b7ed29b073f82..5284b949bb6dbbcbaa5ded4f7c4b1d7fed4d89aa 100644 --- a/models/cv/classification/hrnet_w18/igie/README.md +++ b/models/cv/classification/hrnet_w18/igie/README.md @@ -13,7 +13,7 @@ HRNet, short for High-Resolution Network, presents a paradigm shift in handling ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/hrnet_w18/ixrt/README.md b/models/cv/classification/hrnet_w18/ixrt/README.md index 2d38c4d463e6f06a402c2c8f65c9fd90708dc3b5..93691d2bd07a79fe101c0430a98f0146be27907c 100644 --- a/models/cv/classification/hrnet_w18/ixrt/README.md +++ b/models/cv/classification/hrnet_w18/ixrt/README.md @@ -13,7 +13,7 @@ HRNet-W18 is a powerful image classification model developed by Jingdong AI Rese ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/inception_v3/ixrt/README.md b/models/cv/classification/inception_v3/ixrt/README.md index 0a841b9ffbd1410c6b6a30bb33fd996c8af97a4b..e0d938d3b989c7f1b937e8391003fc124dd65b5e 100755 --- a/models/cv/classification/inception_v3/ixrt/README.md +++ b/models/cv/classification/inception_v3/ixrt/README.md @@ -13,7 +13,7 @@ Inception v3 is a convolutional neural network architecture designed for image r ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/inceptionresnetv2/ixrt/README.md b/models/cv/classification/inceptionresnetv2/ixrt/README.md index e55a13a23ee5de7a231be1bfc46963a7cd3d1159..22da2963e3bf795e490f4a46deb53ed59e2ae75c 100755 --- a/models/cv/classification/inceptionresnetv2/ixrt/README.md +++ b/models/cv/classification/inceptionresnetv2/ixrt/README.md @@ -13,7 +13,7 @@ Inception-ResNetV2 is a deep learning model proposed by Google in 2016, which co ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/mlp_mixer_base/igie/README.md b/models/cv/classification/mlp_mixer_base/igie/README.md index b2e9d47f876a8feffeae58a6fe7f29da7aabb637..d82c728b9298d0c22948a2244e114b56e9221372 100644 --- a/models/cv/classification/mlp_mixer_base/igie/README.md +++ b/models/cv/classification/mlp_mixer_base/igie/README.md @@ -13,7 +13,7 @@ MLP-Mixer Base is a foundational model in the MLP-Mixer family, designed to use ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/mobilenet_v3/ixrt/README.md b/models/cv/classification/mobilenet_v3/ixrt/README.md index 1ff5e158f9764e81c2977df60ef76b1bf6432c42..7a80ec49623d2901b2ea6d1a4c7349a23ec589bd 100644 --- a/models/cv/classification/mobilenet_v3/ixrt/README.md +++ b/models/cv/classification/mobilenet_v3/ixrt/README.md @@ -13,7 +13,7 @@ MobileNetV3 is a convolutional neural network that is tuned to mobile phone CPUs ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/mvitv2_base/igie/README.md b/models/cv/classification/mvitv2_base/igie/README.md index b161bc107ba738f069fa403d41adae35896961a1..c6a112ae03e26f2e232d37fa51f02ed404469bd1 100644 --- a/models/cv/classification/mvitv2_base/igie/README.md +++ b/models/cv/classification/mvitv2_base/igie/README.md @@ -13,7 +13,7 @@ MViTv2_base is an efficient multi-scale vision Transformer model designed specif ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/repvgg/igie/README.md b/models/cv/classification/repvgg/igie/README.md index f71b37ee21a923dfdddc5c62794474c84d496b64..0f02b88d30d724d35fe42ffc181d9c3428069285 100644 --- a/models/cv/classification/repvgg/igie/README.md +++ b/models/cv/classification/repvgg/igie/README.md @@ -13,7 +13,7 @@ RepVGG is an innovative convolutional neural network architecture that combines ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/repvgg/ixrt/README.md b/models/cv/classification/repvgg/ixrt/README.md index e93c626a8c7f26e17dd7b152edd8b02af3682fbc..a1b2a36328a45115352b2ef91641278630e6d930 100644 --- a/models/cv/classification/repvgg/ixrt/README.md +++ b/models/cv/classification/repvgg/ixrt/README.md @@ -14,7 +14,7 @@ It was developed by researchers at the University of Oxford and introduced in th ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/res2net50/igie/README.md b/models/cv/classification/res2net50/igie/README.md index b02548c5bd479913b43fdd72b647c3f77dcf7e23..eb698705924ce0f16d85e963a9171c1989295fe8 100644 --- a/models/cv/classification/res2net50/igie/README.md +++ b/models/cv/classification/res2net50/igie/README.md @@ -13,7 +13,7 @@ Res2Net50 is a convolutional neural network architecture that introduces the con ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/res2net50/ixrt/README.md b/models/cv/classification/res2net50/ixrt/README.md index 1ec7a25194062661cc526a773ca7755aac493b66..6700543f5ce219c793533d37996aeda1bda56e1d 100644 --- a/models/cv/classification/res2net50/ixrt/README.md +++ b/models/cv/classification/res2net50/ixrt/README.md @@ -13,7 +13,7 @@ A novel building block for CNNs, namely Res2Net, by constructing hierarchical re ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnest50/igie/README.md b/models/cv/classification/resnest50/igie/README.md index 3bb04255fb48c16b872df1ea8ca755e60f02067c..21137c42b5c10e13a6a4529018b104ad3699c206 100644 --- a/models/cv/classification/resnest50/igie/README.md +++ b/models/cv/classification/resnest50/igie/README.md @@ -13,7 +13,7 @@ ResNeSt50 is a deep convolutional neural network model based on the ResNeSt arch ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnet101/ixrt/README.md b/models/cv/classification/resnet101/ixrt/README.md index d85e312077a4e26b1711986b3fcd5559ff39a2bf..3869ea55bb571a068854a896a71600d8472fd1ff 100644 --- a/models/cv/classification/resnet101/ixrt/README.md +++ b/models/cv/classification/resnet101/ixrt/README.md @@ -13,7 +13,7 @@ ResNet-101 is a variant of the ResNet (Residual Network) architecture, and it be ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r reuirements.txt ``` diff --git a/models/cv/classification/resnet18/ixrt/README.md b/models/cv/classification/resnet18/ixrt/README.md index 71857e6566130c3daf7787a1314ecc1e947205e3..84ce36f64876ba9fd66a01af5bbdd460658f545e 100644 --- a/models/cv/classification/resnet18/ixrt/README.md +++ b/models/cv/classification/resnet18/ixrt/README.md @@ -13,7 +13,7 @@ ResNet-18 is a variant of the ResNet (Residual Network) architecture, which was ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnet34/ixrt/README.md b/models/cv/classification/resnet34/ixrt/README.md index 8855611eb32940951ea805d628c6a4fac27820f0..48df97d3216c5c17675b80007a504032622648dc 100644 --- a/models/cv/classification/resnet34/ixrt/README.md +++ b/models/cv/classification/resnet34/ixrt/README.md @@ -13,7 +13,7 @@ Residual Networks, or ResNets, learn residual functions with reference to the la ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnet50/ixrt/README.md b/models/cv/classification/resnet50/ixrt/README.md index 91700b76d3bf391da4492d5ab6bb9434aea092a7..0d5fc9fa2347e50e7eb067d76f3d6af583477770 100644 --- a/models/cv/classification/resnet50/ixrt/README.md +++ b/models/cv/classification/resnet50/ixrt/README.md @@ -13,7 +13,7 @@ Residual Networks, or ResNets, learn residual functions with reference to the la ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnetv1d50/ixrt/README.md b/models/cv/classification/resnetv1d50/ixrt/README.md index 3b49e01e335238b7ba4a66afb42f9f64331f1a3c..7445f4581d62d622e08d0bb0de2072d85252fcd2 100644 --- a/models/cv/classification/resnetv1d50/ixrt/README.md +++ b/models/cv/classification/resnetv1d50/ixrt/README.md @@ -13,7 +13,7 @@ Residual Networks, or ResNets, learn residual functions with reference to the la ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirments.txt ``` diff --git a/models/cv/classification/seresnet50/igie/README.md b/models/cv/classification/seresnet50/igie/README.md index 876f13dd83cdb4f93f1b1cf790adb74d18a05bc2..ddd891ec083a848bcb2fd24e567df59c106175eb 100644 --- a/models/cv/classification/seresnet50/igie/README.md +++ b/models/cv/classification/seresnet50/igie/README.md @@ -13,7 +13,7 @@ SEResNet50 is an enhanced version of the ResNet50 network integrated with Squeez ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/shufflenet_v1/ixrt/README.md b/models/cv/classification/shufflenet_v1/ixrt/README.md index 94c884328cdeaa9076fe3bcac29b41603d2751e7..ae50050c073f565f67f7353049af5ede9b538f13 100644 --- a/models/cv/classification/shufflenet_v1/ixrt/README.md +++ b/models/cv/classification/shufflenet_v1/ixrt/README.md @@ -14,7 +14,7 @@ It uses techniques such as deep separable convolution and channel shuffle to red ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/squeezenet_v1_0/ixrt/README.md b/models/cv/classification/squeezenet_v1_0/ixrt/README.md index 6af82041a9d801ec51b70b083eadd78483d228bf..faea4b18eea97de42f508ed03f2b361f821723ea 100644 --- a/models/cv/classification/squeezenet_v1_0/ixrt/README.md +++ b/models/cv/classification/squeezenet_v1_0/ixrt/README.md @@ -15,7 +15,7 @@ It was developed by researchers at DeepScale and released in 2016. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/squeezenet_v1_1/ixrt/README.md b/models/cv/classification/squeezenet_v1_1/ixrt/README.md index eb6126c373534243adee03bb7b433ab7542b3cec..ada0742aacf510b66c53b37c55857db30a1f63a1 100644 --- a/models/cv/classification/squeezenet_v1_1/ixrt/README.md +++ b/models/cv/classification/squeezenet_v1_1/ixrt/README.md @@ -15,7 +15,7 @@ It was developed by researchers at DeepScale and released in 2016. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/svt_base/igie/README.md b/models/cv/classification/svt_base/igie/README.md index 98dd7791e16c230c1123a4772eebfe8329237d45..26d020d0f5d35478678de98e807cca2b42aa1888 100644 --- a/models/cv/classification/svt_base/igie/README.md +++ b/models/cv/classification/svt_base/igie/README.md @@ -13,7 +13,7 @@ SVT Base is a mid-sized variant of the Sparse Vision Transformer (SVT) series, d ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/vgg16/ixrt/README.md b/models/cv/classification/vgg16/ixrt/README.md index 789fcc8b3684285c58795ecd92fb93f707817dd2..7681a924aae81e772138b10546a408b08db9f31d 100644 --- a/models/cv/classification/vgg16/ixrt/README.md +++ b/models/cv/classification/vgg16/ixrt/README.md @@ -14,7 +14,7 @@ It finished second in the 2014 ImageNet Massive Visual Identity Challenge (ILSVR ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/atss/igie/README.md b/models/cv/detection/atss/igie/README.md index 3d887918da73aa9d976754aeffbc819da1c0a837..23f0ae94ca2824f4c4c876bcc5a5d880e840bbde 100644 --- a/models/cv/detection/atss/igie/README.md +++ b/models/cv/detection/atss/igie/README.md @@ -13,7 +13,7 @@ ATSS is an advanced adaptive training sample selection method that effectively e ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/centernet/igie/README.md b/models/cv/detection/centernet/igie/README.md index d398784059ceb08c2d5fd54055fa1ad618fc0a13..64156b2edcc94f154eec2fca25775005ff4605e5 100644 --- a/models/cv/detection/centernet/igie/README.md +++ b/models/cv/detection/centernet/igie/README.md @@ -13,7 +13,7 @@ CenterNet is an efficient object detection model that simplifies the traditional ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/centernet/ixrt/README.md b/models/cv/detection/centernet/ixrt/README.md index 8978b2b49daa7daa333cd113d5fe2259d2222687..776b54fb257d9394cd9caafe36ed022f3019d356 100644 --- a/models/cv/detection/centernet/ixrt/README.md +++ b/models/cv/detection/centernet/ixrt/README.md @@ -13,7 +13,7 @@ CenterNet is an efficient object detection model that simplifies the traditional ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt # Contact the Iluvatar administrator to get the mmcv install package. diff --git a/models/cv/detection/detr/ixrt/README.md b/models/cv/detection/detr/ixrt/README.md index 1704b35c8ddc267bcbba2867770fd8f498c8f521..cde0acdaffda41ffc0f3f0f83229cf82fb501003 100755 --- a/models/cv/detection/detr/ixrt/README.md +++ b/models/cv/detection/detr/ixrt/README.md @@ -13,7 +13,7 @@ DETR (DEtection TRansformer) is a novel approach that views object detection as ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/fcos/igie/README.md b/models/cv/detection/fcos/igie/README.md index c042feac01244fd09ec4715d4f62147da858419b..2bcdbee2a63fc95e28cc608c5798ac8262c18f87 100644 --- a/models/cv/detection/fcos/igie/README.md +++ b/models/cv/detection/fcos/igie/README.md @@ -13,7 +13,7 @@ FCOS is an innovative one-stage object detection framework that abandons traditi ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/fcos/ixrt/README.md b/models/cv/detection/fcos/ixrt/README.md index bc3f046116bdecc90c74ee0f88e5abc1c1fc488b..56b18c6980bfbc5a1689eaf26b9b002e25b296db 100755 --- a/models/cv/detection/fcos/ixrt/README.md +++ b/models/cv/detection/fcos/ixrt/README.md @@ -14,7 +14,7 @@ For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/19 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/foveabox/igie/README.md b/models/cv/detection/foveabox/igie/README.md index aded8a5cd202dcdaa44f5d7fa30fb37877f56724..cbed39fddb63ae831cc5b07172c93dccf10ab0df 100644 --- a/models/cv/detection/foveabox/igie/README.md +++ b/models/cv/detection/foveabox/igie/README.md @@ -13,7 +13,7 @@ FoveaBox is an advanced anchor-free object detection framework that enhances acc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/foveabox/ixrt/README.md b/models/cv/detection/foveabox/ixrt/README.md index d86a2c3961f6ac2da5700d345380ab4415331f6b..2682a25328e027e5ebadaa62b187a55694f9cfab 100644 --- a/models/cv/detection/foveabox/ixrt/README.md +++ b/models/cv/detection/foveabox/ixrt/README.md @@ -13,7 +13,7 @@ FoveaBox is an advanced anchor-free object detection framework that enhances acc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/detection/fsaf/igie/README.md b/models/cv/detection/fsaf/igie/README.md index 6df84b12a61acec66a0a423298c104a426725805..d6fdbcfd85f55cd9458ecf86df433e595e1c82ae 100644 --- a/models/cv/detection/fsaf/igie/README.md +++ b/models/cv/detection/fsaf/igie/README.md @@ -13,7 +13,7 @@ The FSAF (Feature Selective Anchor-Free) module is an innovative component for s ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/fsaf/ixrt/README.md b/models/cv/detection/fsaf/ixrt/README.md index 5d75a2e6e3d0df468786d9f99a416b331c59d8c5..03935e629b1cfe63a91dd566577a522902690e50 100644 --- a/models/cv/detection/fsaf/ixrt/README.md +++ b/models/cv/detection/fsaf/ixrt/README.md @@ -13,7 +13,7 @@ The FSAF (Feature Selective Anchor-Free) module is an innovative component for s ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/hrnet/igie/README.md b/models/cv/detection/hrnet/igie/README.md index e0966ccd7464031a57bdf73b3fd22cdf32f9603c..49fc4dcd00fa07afdd156791253463487d1f5000 100644 --- a/models/cv/detection/hrnet/igie/README.md +++ b/models/cv/detection/hrnet/igie/README.md @@ -13,7 +13,7 @@ HRNet is an advanced deep learning architecture for human pose estimation, chara ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/hrnet/ixrt/README.md b/models/cv/detection/hrnet/ixrt/README.md index cade087c4e00dac7b92db9943932facf97ba04e4..284faafd0497c095e25ac375bfd652c87a496b46 100644 --- a/models/cv/detection/hrnet/ixrt/README.md +++ b/models/cv/detection/hrnet/ixrt/README.md @@ -13,7 +13,7 @@ HRNet is an advanced deep learning architecture for human pose estimation, chara ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/paa/igie/README.md b/models/cv/detection/paa/igie/README.md index ddf47326c609c413906a268e46ab06ebf43cea3a..5cbc5505c4d072f5bc881c863e091827bac3011e 100644 --- a/models/cv/detection/paa/igie/README.md +++ b/models/cv/detection/paa/igie/README.md @@ -13,7 +13,7 @@ PAA (Probabilistic Anchor Assignment) is an algorithm for object detection that ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/retinaface/igie/README.md b/models/cv/detection/retinaface/igie/README.md index 82d127ec006dc6d8324023b99a613382fdaff7d5..3610a8bf666b55cf053afb8cd0dc9149135fc98b 100755 --- a/models/cv/detection/retinaface/igie/README.md +++ b/models/cv/detection/retinaface/igie/README.md @@ -13,7 +13,7 @@ RetinaFace is an efficient single-stage face detection model that employs a mult ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/retinaface/ixrt/README.md b/models/cv/detection/retinaface/ixrt/README.md index 8fff5439fd8e82e836e58abc7c38296f0ec27db5..2513eb34d69f52137d8b022b8ed157574a6b0032 100644 --- a/models/cv/detection/retinaface/ixrt/README.md +++ b/models/cv/detection/retinaface/ixrt/README.md @@ -13,7 +13,7 @@ RetinaFace is an efficient single-stage face detection model that employs a mult ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt diff --git a/models/cv/detection/retinanet/igie/README.md b/models/cv/detection/retinanet/igie/README.md index 648ad5d097debe29d659fdf4b89426c5289a68e8..18067d85f6bd296aca7a3b00694a5765564a1fb7 100644 --- a/models/cv/detection/retinanet/igie/README.md +++ b/models/cv/detection/retinanet/igie/README.md @@ -13,7 +13,7 @@ RetinaNet, an innovative object detector, challenges the conventional trade-off ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/rtmdet/igie/README.md b/models/cv/detection/rtmdet/igie/README.md index 3b2526d1224ad06c8611751e10f90f2211d05e86..8ed35963b1af9faf66346bd01b6e7316f2ed18f5 100644 --- a/models/cv/detection/rtmdet/igie/README.md +++ b/models/cv/detection/rtmdet/igie/README.md @@ -13,7 +13,7 @@ RTMDet, presented by the Shanghai AI Laboratory, is a novel framework for real-t ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/sabl/igie/README.md b/models/cv/detection/sabl/igie/README.md index 28d0242e2905f30d1f520a3d8f4245fe86ccf5f5..25732e82c79af5130609848962a9045f25a5d0b9 100644 --- a/models/cv/detection/sabl/igie/README.md +++ b/models/cv/detection/sabl/igie/README.md @@ -13,7 +13,7 @@ SABL (Side-Aware Boundary Localization) is an innovative approach in object dete ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov3/igie/README.md b/models/cv/detection/yolov3/igie/README.md index e0c696d69605861cab631ac416a4034e2a5108ca..7e1d769afe6584d0eb08af2202023f3432ee7ccd 100644 --- a/models/cv/detection/yolov3/igie/README.md +++ b/models/cv/detection/yolov3/igie/README.md @@ -13,7 +13,7 @@ YOLOv3 is a influential object detection algorithm.The key innovation of YOLOv3 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov3/ixrt/README.md b/models/cv/detection/yolov3/ixrt/README.md index fc531ab36cd91f262dec9aa57c373362b10380f1..d1f37fec61e169da484a63de689f4484a27ef4b2 100644 --- a/models/cv/detection/yolov3/ixrt/README.md +++ b/models/cv/detection/yolov3/ixrt/README.md @@ -13,7 +13,7 @@ YOLOv3 is a influential object detection algorithm.The key innovation of YOLOv3 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov4/igie/README.md b/models/cv/detection/yolov4/igie/README.md index a88ea5b324e538072d6a4c1fea255633a54fe6b5..f2a0320d1fe1263f1d136b2ffd69dee351cd70a5 100644 --- a/models/cv/detection/yolov4/igie/README.md +++ b/models/cv/detection/yolov4/igie/README.md @@ -13,7 +13,7 @@ YOLOv4 employs a two-step process, involving regression for bounding box positio ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov4/ixrt/README.md b/models/cv/detection/yolov4/ixrt/README.md index ae94ae43aeaeff1dd01d6c0f3bf7d5b9f7c019f5..8b53a7527041d56cfb18873474fcd99d80036cf4 100644 --- a/models/cv/detection/yolov4/ixrt/README.md +++ b/models/cv/detection/yolov4/ixrt/README.md @@ -13,7 +13,7 @@ YOLOv4 employs a two-step process, involving regression for bounding box positio ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov5/igie/README.md b/models/cv/detection/yolov5/igie/README.md index f8d7d5a7ec52567c877d3edaefd074fbcb2f0c24..f71018d449f2f21c2621f50da02bc5db04fd28ec 100644 --- a/models/cv/detection/yolov5/igie/README.md +++ b/models/cv/detection/yolov5/igie/README.md @@ -13,7 +13,7 @@ The YOLOv5 architecture is designed for efficient and accurate object detection ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov5/ixrt/README.md b/models/cv/detection/yolov5/ixrt/README.md index ea1fc1801a78b35007fad7c3231216e9b9ccd862..fd4e7e0b6ab22676d278d8aff82fca9e446877db 100644 --- a/models/cv/detection/yolov5/ixrt/README.md +++ b/models/cv/detection/yolov5/ixrt/README.md @@ -13,7 +13,7 @@ The YOLOv5 architecture is designed for efficient and accurate object detection ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov5s/ixrt/README.md b/models/cv/detection/yolov5s/ixrt/README.md index 3847db5a56bb3723c52fbeaf4484773674998cb1..cf048372b410f658c14f65a3ab03605e9cf29529 100755 --- a/models/cv/detection/yolov5s/ixrt/README.md +++ b/models/cv/detection/yolov5s/ixrt/README.md @@ -13,7 +13,7 @@ The YOLOv5 architecture is designed for efficient and accurate object detection ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov7/igie/README.md b/models/cv/detection/yolov7/igie/README.md index 3d6cd5fa86a44a84bbcbdee84acb67215ef11a76..d23f22d644f6fdc9cb0c4dbde87008f55cf3bf82 100644 --- a/models/cv/detection/yolov7/igie/README.md +++ b/models/cv/detection/yolov7/igie/README.md @@ -13,7 +13,7 @@ YOLOv7 is a state-of-the-art real-time object detector that surpasses all known ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov7/ixrt/README.md b/models/cv/detection/yolov7/ixrt/README.md index 7b6e91c3e9cbc039f008dfccbd49117a406408f3..2174c199b93a12950e97eeea9df36cb9d57afa20 100644 --- a/models/cv/detection/yolov7/ixrt/README.md +++ b/models/cv/detection/yolov7/ixrt/README.md @@ -13,7 +13,7 @@ YOLOv7 is an object detection model based on the YOLO (You Only Look Once) serie ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov8/igie/README.md b/models/cv/detection/yolov8/igie/README.md index 36fab0348e478b60d3919ff1c2a980f4c8a701f5..b3ee9d5ef9744d44c6b64c8f7b8cc4059efc47ea 100644 --- a/models/cv/detection/yolov8/igie/README.md +++ b/models/cv/detection/yolov8/igie/README.md @@ -13,7 +13,7 @@ Yolov8 combines speed and accuracy in real-time object detection tasks. With a f ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolox/igie/README.md b/models/cv/detection/yolox/igie/README.md index a1de586395bea2e39303884a91213e01f30216d7..32c3d6389e248739684608afa69fc4650009f7ec 100644 --- a/models/cv/detection/yolox/igie/README.md +++ b/models/cv/detection/yolox/igie/README.md @@ -13,7 +13,7 @@ YOLOX is an anchor-free version of YOLO, with a simpler design but better perfor ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt diff --git a/models/cv/detection/yolox/ixrt/README.md b/models/cv/detection/yolox/ixrt/README.md index 475a5002336161ee3fe81349491a6472c2d55680..0a19d5bf2873f20b501ab0bce6eb89a8f072c387 100644 --- a/models/cv/detection/yolox/ixrt/README.md +++ b/models/cv/detection/yolox/ixrt/README.md @@ -14,7 +14,7 @@ For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/21 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/hrnetpose/igie/README.md b/models/cv/pose_estimation/hrnetpose/igie/README.md index c4f0758fd794e3d6a553955697b9334929d99b65..09771216b84e9066329ecdeb2c14c95c10b21a50 100644 --- a/models/cv/pose_estimation/hrnetpose/igie/README.md +++ b/models/cv/pose_estimation/hrnetpose/igie/README.md @@ -13,7 +13,7 @@ HRNetPose (High-Resolution Network for Pose Estimation) is a high-performance hu ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md b/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md index ca18417a413f950ceba19860c42cf7bd671c8117..4fa8923c7770e8cbc2746bbdcaa9db28116b9956 100644 --- a/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md +++ b/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md @@ -13,7 +13,7 @@ This work heavily optimizes the OpenPose approach to reach real-time inference o ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/rtmpose/igie/README.md b/models/cv/pose_estimation/rtmpose/igie/README.md index dd5657002493ed402df53acb21976486a370492b..9497dcfdb4089c7d2eae913b64f7e8945339a4fe 100644 --- a/models/cv/pose_estimation/rtmpose/igie/README.md +++ b/models/cv/pose_estimation/rtmpose/igie/README.md @@ -13,7 +13,7 @@ RTMPose, a state-of-the-art framework developed by Shanghai AI Laboratory, excel ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/rtmpose/ixrt/README.md b/models/cv/pose_estimation/rtmpose/ixrt/README.md index ea69ea32d3566bcca744e89342cafd95be05b4d7..51a832a181156ee205e6e1fbd70b40c612b04b18 100644 --- a/models/cv/pose_estimation/rtmpose/ixrt/README.md +++ b/models/cv/pose_estimation/rtmpose/ixrt/README.md @@ -13,7 +13,7 @@ RTMPose, a state-of-the-art framework developed by Shanghai AI Laboratory, excel ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/multimodal/diffusion/stable-diffusion/README.md b/models/multimodal/diffusion_model/stable-diffusion/README.md similarity index 100% rename from models/multimodal/diffusion/stable-diffusion/README.md rename to models/multimodal/diffusion_model/stable-diffusion/README.md diff --git a/models/multimodal/diffusion/stable-diffusion/ci/prepare.sh b/models/multimodal/diffusion_model/stable-diffusion/ci/prepare.sh similarity index 100% rename from models/multimodal/diffusion/stable-diffusion/ci/prepare.sh rename to models/multimodal/diffusion_model/stable-diffusion/ci/prepare.sh diff --git a/models/multimodal/diffusion/stable-diffusion/demo.py b/models/multimodal/diffusion_model/stable-diffusion/demo.py similarity index 100% rename from models/multimodal/diffusion/stable-diffusion/demo.py rename to models/multimodal/diffusion_model/stable-diffusion/demo.py diff --git a/models/multimodal/diffusion/stable-diffusion/requirements.txt b/models/multimodal/diffusion_model/stable-diffusion/requirements.txt similarity index 100% rename from models/multimodal/diffusion/stable-diffusion/requirements.txt rename to models/multimodal/diffusion_model/stable-diffusion/requirements.txt diff --git a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md b/models/multimodal/vision_language_model/chameleon_7b/vllm/README.md similarity index 95% rename from models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md rename to models/multimodal/vision_language_model/chameleon_7b/vllm/README.md index 568dbd7011ff4bf157b4a0e2ee027729f192f9a0..879367dd8f3b0b3a069ddfb684db4d9967eecb3f 100755 --- a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md +++ b/models/multimodal/vision_language_model/chameleon_7b/vllm/README.md @@ -15,12 +15,12 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/chameleon-7b" diff --git a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/offline_inference_vision_language.py b/models/multimodal/vision_language_model/chameleon_7b/vllm/offline_inference_vision_language.py similarity index 100% rename from models/multimodal/vision_language_understanding/chameleon_7b/vllm/offline_inference_vision_language.py rename to models/multimodal/vision_language_model/chameleon_7b/vllm/offline_inference_vision_language.py diff --git a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/utils.py b/models/multimodal/vision_language_model/chameleon_7b/vllm/utils.py similarity index 100% rename from models/multimodal/vision_language_understanding/chameleon_7b/vllm/utils.py rename to models/multimodal/vision_language_model/chameleon_7b/vllm/utils.py diff --git a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/vllm_public_assets/cherry_blossom.jpg b/models/multimodal/vision_language_model/chameleon_7b/vllm/vllm_public_assets/cherry_blossom.jpg similarity index 100% rename from models/multimodal/vision_language_understanding/chameleon_7b/vllm/vllm_public_assets/cherry_blossom.jpg rename to models/multimodal/vision_language_model/chameleon_7b/vllm/vllm_public_assets/cherry_blossom.jpg diff --git a/models/multimodal/vision_language_understanding/clip/ixformer/README.md b/models/multimodal/vision_language_model/clip/ixformer/README.md similarity index 97% rename from models/multimodal/vision_language_understanding/clip/ixformer/README.md rename to models/multimodal/vision_language_model/clip/ixformer/README.md index b180a04551c9abe831009277abee9f960ed18da3..e8b90f57f627546dae5e847660cf1d2b97c74fd2 100644 --- a/models/multimodal/vision_language_understanding/clip/ixformer/README.md +++ b/models/multimodal/vision_language_model/clip/ixformer/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -U transformers==4.27.1 ``` diff --git a/models/multimodal/vision_language_understanding/clip/ixformer/ci/prepare.sh b/models/multimodal/vision_language_model/clip/ixformer/ci/prepare.sh similarity index 100% rename from models/multimodal/vision_language_understanding/clip/ixformer/ci/prepare.sh rename to models/multimodal/vision_language_model/clip/ixformer/ci/prepare.sh diff --git a/models/multimodal/vision_language_understanding/clip/ixformer/inference.py b/models/multimodal/vision_language_model/clip/ixformer/inference.py similarity index 100% rename from models/multimodal/vision_language_understanding/clip/ixformer/inference.py rename to models/multimodal/vision_language_model/clip/ixformer/inference.py diff --git a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md b/models/multimodal/vision_language_model/fuyu_8b/vllm/README.md similarity index 92% rename from models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md rename to models/multimodal/vision_language_model/fuyu_8b/vllm/README.md index 96d5acc3b208b72de34624acb74849a7536670d0..7f2526b92a56f02ac9eccb4723a0891a2bf75780 100755 --- a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md +++ b/models/multimodal/vision_language_model/fuyu_8b/vllm/README.md @@ -17,12 +17,12 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/fuyu-8b" diff --git a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/offline_inference_vision_language.py b/models/multimodal/vision_language_model/fuyu_8b/vllm/offline_inference_vision_language.py similarity index 100% rename from models/multimodal/vision_language_understanding/fuyu_8b/vllm/offline_inference_vision_language.py rename to models/multimodal/vision_language_model/fuyu_8b/vllm/offline_inference_vision_language.py diff --git a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/utils.py b/models/multimodal/vision_language_model/fuyu_8b/vllm/utils.py similarity index 100% rename from models/multimodal/vision_language_understanding/fuyu_8b/vllm/utils.py rename to models/multimodal/vision_language_model/fuyu_8b/vllm/utils.py diff --git a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/vllm_public_assets/cherry_blossom.jpg b/models/multimodal/vision_language_model/fuyu_8b/vllm/vllm_public_assets/cherry_blossom.jpg similarity index 100% rename from models/multimodal/vision_language_understanding/fuyu_8b/vllm/vllm_public_assets/cherry_blossom.jpg rename to models/multimodal/vision_language_model/fuyu_8b/vllm/vllm_public_assets/cherry_blossom.jpg diff --git a/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md b/models/multimodal/vision_language_model/intern_vl/vllm/README.md similarity index 93% rename from models/multimodal/vision_language_understanding/intern_vl/vllm/README.md rename to models/multimodal/vision_language_model/intern_vl/vllm/README.md index cafe64f99ad80d3602a3ba3f478ae6b1f8cdb3fb..c8a527798abc8aa315b316e53f3879dc87538e11 100644 --- a/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md +++ b/models/multimodal/vision_language_model/intern_vl/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx # Contact the iluvatar manager to get adapted install packages of vllm, triton, and ixformer pip3 install vllm @@ -25,7 +25,7 @@ pip3 install ixformer ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/vision-language-understanding/Intern_VL/vllm diff --git a/models/multimodal/vision_language_understanding/intern_vl/vllm/offline_inference_vision_language.py b/models/multimodal/vision_language_model/intern_vl/vllm/offline_inference_vision_language.py similarity index 100% rename from models/multimodal/vision_language_understanding/intern_vl/vllm/offline_inference_vision_language.py rename to models/multimodal/vision_language_model/intern_vl/vllm/offline_inference_vision_language.py diff --git a/models/multimodal/vision_language_understanding/intern_vl/vllm/utils.py b/models/multimodal/vision_language_model/intern_vl/vllm/utils.py similarity index 100% rename from models/multimodal/vision_language_understanding/intern_vl/vllm/utils.py rename to models/multimodal/vision_language_model/intern_vl/vllm/utils.py diff --git a/models/multimodal/vision_language_understanding/intern_vl/vllm/vllm_public_assets/cherry_blossom.jpg b/models/multimodal/vision_language_model/intern_vl/vllm/vllm_public_assets/cherry_blossom.jpg similarity index 100% rename from models/multimodal/vision_language_understanding/intern_vl/vllm/vllm_public_assets/cherry_blossom.jpg rename to models/multimodal/vision_language_model/intern_vl/vllm/vllm_public_assets/cherry_blossom.jpg diff --git a/models/multimodal/vision_language_understanding/llava/vllm/README.md b/models/multimodal/vision_language_model/llava/vllm/README.md similarity index 98% rename from models/multimodal/vision_language_understanding/llava/vllm/README.md rename to models/multimodal/vision_language_model/llava/vllm/README.md index bbc251d2b3766dde38214515217f0faf75085d12..2ceeaed8182b2b75386647957d89b9eb0c2d6bba 100644 --- a/models/multimodal/vision_language_understanding/llava/vllm/README.md +++ b/models/multimodal/vision_language_model/llava/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install transformers ``` diff --git a/models/multimodal/vision_language_understanding/llava/vllm/offline_inference_vision_language.py b/models/multimodal/vision_language_model/llava/vllm/offline_inference_vision_language.py similarity index 100% rename from models/multimodal/vision_language_understanding/llava/vllm/offline_inference_vision_language.py rename to models/multimodal/vision_language_model/llava/vllm/offline_inference_vision_language.py diff --git a/models/multimodal/vision_language_understanding/llava/vllm/utils.py b/models/multimodal/vision_language_model/llava/vllm/utils.py similarity index 100% rename from models/multimodal/vision_language_understanding/llava/vllm/utils.py rename to models/multimodal/vision_language_model/llava/vllm/utils.py diff --git a/models/multimodal/vision_language_understanding/llava/vllm/vllm_public_assets/cherry_blossom.jpg b/models/multimodal/vision_language_model/llava/vllm/vllm_public_assets/cherry_blossom.jpg similarity index 100% rename from models/multimodal/vision_language_understanding/llava/vllm/vllm_public_assets/cherry_blossom.jpg rename to models/multimodal/vision_language_model/llava/vllm/vllm_public_assets/cherry_blossom.jpg diff --git a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md b/models/multimodal/vision_language_model/llava_next_video_7b/vllm/README.md similarity index 91% rename from models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md rename to models/multimodal/vision_language_model/llava_next_video_7b/vllm/README.md index bf4b268310243ce6b51b23efdbf0d97aed5a573c..4fb09f4f6b85e27f1cd84b8c9833fd5aee08111e 100755 --- a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md +++ b/models/multimodal/vision_language_model/llava_next_video_7b/vllm/README.md @@ -15,12 +15,12 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/LLaVA-NeXT-Video-7B-hf" diff --git a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/offline_inference_vision_language.py b/models/multimodal/vision_language_model/llava_next_video_7b/vllm/offline_inference_vision_language.py similarity index 100% rename from models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/offline_inference_vision_language.py rename to models/multimodal/vision_language_model/llava_next_video_7b/vllm/offline_inference_vision_language.py diff --git a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/utils.py b/models/multimodal/vision_language_model/llava_next_video_7b/vllm/utils.py similarity index 100% rename from models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/utils.py rename to models/multimodal/vision_language_model/llava_next_video_7b/vllm/utils.py diff --git a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/video-eample-data/sample_demo_1.mp4 b/models/multimodal/vision_language_model/llava_next_video_7b/vllm/video-eample-data/sample_demo_1.mp4 similarity index 100% rename from models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/video-eample-data/sample_demo_1.mp4 rename to models/multimodal/vision_language_model/llava_next_video_7b/vllm/video-eample-data/sample_demo_1.mp4 diff --git a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md b/models/multimodal/vision_language_model/minicpm_v_2/vllm/README.md similarity index 94% rename from models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md rename to models/multimodal/vision_language_model/minicpm_v_2/vllm/README.md index 149f01f15dac0c9b701b1f0cba9ad41bc242da9e..d2b2dd8689d18c2d2686560da07c6d362d3449cb 100644 --- a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md +++ b/models/multimodal/vision_language_model/minicpm_v_2/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install timm==0.9.10 pip3 install transformers pip3 install --user --upgrade pillow -i https://pypi.tuna.tsinghua.edu.cn/simple @@ -23,7 +23,7 @@ pip3 install --user --upgrade pillow -i https://pypi.tuna.tsinghua.edu.cn/simple ### Download --Model: +- Model: Note: Due to the official weights missing some necessary files for vllm execution, you can download the additional files from here: to ensure that the file directory matches the structure shown here: . ```bash diff --git a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/ci/prepare.sh b/models/multimodal/vision_language_model/minicpm_v_2/vllm/ci/prepare.sh similarity index 100% rename from models/multimodal/vision_language_understanding/minicpm_v_2/vllm/ci/prepare.sh rename to models/multimodal/vision_language_model/minicpm_v_2/vllm/ci/prepare.sh diff --git a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/minicpmv-2.0-offline.py b/models/multimodal/vision_language_model/minicpm_v_2/vllm/minicpmv-2.0-offline.py similarity index 100% rename from models/multimodal/vision_language_understanding/minicpm_v_2/vllm/minicpmv-2.0-offline.py rename to models/multimodal/vision_language_model/minicpm_v_2/vllm/minicpmv-2.0-offline.py diff --git a/models/nlp/language_model/bert_large_squad/ixrt/README.md b/models/nlp/language_model/bert_large_squad/ixrt/README.md index b824a8ee8d099922605edbb2ee0ff4ae4d559746..a8bcf5b7957f62b71142ea6e0527f5bfacc5ce59 100644 --- a/models/nlp/language_model/bert_large_squad/ixrt/README.md +++ b/models/nlp/language_model/bert_large_squad/ixrt/README.md @@ -6,6 +6,9 @@ BERT is designed to pre-train deep bidirectional representations from unlabeled ## Setup +Get `bert-large-uncased.zip` from [Google +Drive](https://drive.google.com/file/d/1eD8QBkbK6YN-_YXODp3tmpp3cZKlrPTA/view?usp=drive_link) + ### NV requirement(tensorrt_version >= 8.6) ```bash @@ -59,14 +62,10 @@ pip install onnx pycuda bash script/build_engine.sh --bs 32 --int8 bash script/inference_squad.sh --bs 32 --int8 ``` - -## Results - | Model | BatchSize | Precision | Latency QPS | exact_match | f1 | -| ---------------- | --------- | --------- | ------------------- | ----------- | ----- | +|------------------|-----------|-----------|---------------------|-------------|-------| +| BERT-Large-SQuAD | 32 | FP16 | 470.26 sentences/s | 82.36 | 89.68 | +| BERT-Large-SQuAD | 32 | INT8 | 1490.47 sentences/s | 80.92 | 88.20 | +|------------------|-----------|-----------|---------------------|-------------|-------| | BERT-Large-SQuAD | 32 | FP16 | 470.26 sentences/s | 82.36 | 89.68 | | BERT-Large-SQuAD | 32 | INT8 | 1490.47 sentences/s | 80.92 | 88.20 | - -## Referenece - -- [bert-large-uncased.zip](https://drive.google.com/file/d/1eD8QBkbK6YN-_YXODp3tmpp3cZKlrPTA/view?usp=drive_link) diff --git a/models/nlp/language_model/deberta/ixrt/README.md b/models/nlp/language_model/deberta/ixrt/README.md index fd1757cd9235cf28fca87ff7ccd60371a43a6b8e..3026d51da214bccf5be647cf60a655e2ddf3142e 100644 --- a/models/nlp/language_model/deberta/ixrt/README.md +++ b/models/nlp/language_model/deberta/ixrt/README.md @@ -2,7 +2,12 @@ ## Description -DeBERTa (Decoding-enhanced BERT with disentangled attention) is an enhanced version of the BERT (Bidirectional Encoder Representations from Transformers) model. It improves text representation learning by introducing disentangled attention mechanisms and decoding enhancement techniques.DeBERTa introduces disentangled attention mechanisms that decompose the self-attention matrix into different parts, focusing on different semantic information. This helps the model better capture relationships between texts.By incorporating decoding enhancement techniques, DeBERTa adjusts the decoder during fine-tuning to better suit specific downstream tasks, thereby improving the model’s performance on those tasks. +DeBERTa (Decoding-enhanced BERT with disentangled attention) is an enhanced version of the BERT (Bidirectional Encoder +Representations from Transformers) model. It improves text representation learning by introducing disentangled attention +mechanisms and decoding enhancement techniques.DeBERTa introduces disentangled attention mechanisms that decompose the +self-attention matrix into different parts, focusing on different semantic information. This helps the model better +capture relationships between texts.By incorporating decoding enhancement techniques, DeBERTa adjusts the decoder during +fine-tuning to better suit specific downstream tasks, thereby improving the model’s performance on those tasks. ## Setup @@ -57,9 +62,13 @@ bash scripts/infer_deberta_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: < >, which integrates inference and training of many models under this framework, supporting the ILUVATAR backend +If you want to evaluate the accuracy of this model, please visit the website: < + >, which integrates inference and training of many +models under this framework, supporting the ILUVATAR backend -For detailed steps regarding this model, please refer to this document: < > Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: < + +> Note: You need to modify the relevant paths in the code to your own correct paths. ```bash # link and install requirements @@ -90,5 +99,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task deberta-torch-fp32 ## Results | Model | BatchSize | Precision | QPS | Exact Match | F1 Score | -| ------- | --------- | --------- | ----- | ----------- | -------- | +|---------|-----------|-----------|-------|-------------|----------| | DeBERTa | 1 | FP16 | 18.58 | 73.76 | 81.24 | diff --git a/models/nlp/language_model/roberta/ixrt/README.md b/models/nlp/language_model/roberta/ixrt/README.md index f37d5f3c0caa21984c992f3d4ca04367b567f2d5..4e45bfa80e0191dea245aa6382201ce09157ae96 100644 --- a/models/nlp/language_model/roberta/ixrt/README.md +++ b/models/nlp/language_model/roberta/ixrt/README.md @@ -2,7 +2,14 @@ ## Description -Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code. +Language model pretraining has led to significant performance gains but careful comparison between different approaches +is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we +will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT +pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. +We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after +it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of +previously overlooked design choices, and raise questions about the source of recently reported improvements. We release +our models and code. ## Setup @@ -59,9 +66,13 @@ bash scripts/infer_roberta_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: , which integrates inference and training of many models under this framework, supporting the ILUVATAR backend +If you want to evaluate the accuracy of this model, please visit the website: +, which integrates inference and training of many +models under this framework, supporting the ILUVATAR backend -For detailed steps regarding this model, please refer to this document: Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: + +Note: You need to modify the relevant paths in the code to your own correct paths. ```bash # Link and install requirements @@ -95,5 +106,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task roberta-torch-fp32 ## Results | Model | BatchSize | Precision | FPS | F1 | Exact Match | -| ------- | --------- | --------- | ------ | -------- | ----------- | +|---------|-----------|-----------|--------|----------|-------------| | RoBERTa | 1 | FP16 | 355.48 | 83.14387 | 76.50175 | diff --git a/models/nlp/language_model/roformer/ixrt/README.md b/models/nlp/language_model/roformer/ixrt/README.md index 59750879f55e8ac896bcbd84554f5da32271a9b4..6d125955c49a925e3bbf73c09406b5c24a0436c9 100644 --- a/models/nlp/language_model/roformer/ixrt/README.md +++ b/models/nlp/language_model/roformer/ixrt/README.md @@ -2,7 +2,16 @@ ## Description -Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in self-attention formulation. Notably, RoPE enables valuable properties, including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping the linear self-attention with relative position encoding. Finally, we evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets. +Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for +dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various +methods to integrate positional information into the learning process of transformer-based language models. Then, we +propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. +Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the +explicit relative position dependency in self-attention formulation. Notably, RoPE enables valuable properties, +including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and +the capability of equipping the linear self-attention with relative position encoding. Finally, we evaluate the enhanced +transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark +datasets. ## Setup @@ -65,9 +74,13 @@ bash scripts/infer_roformer_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: , which integrates inference and training of many models under this framework, supporting the ILUVATAR backend. +If you want to evaluate the accuracy of this model, please visit the website: +, which integrates inference and training of many +models under this framework, supporting the ILUVATAR backend. -For detailed steps regarding this model, please refer to this document: Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: + +Note: You need to modify the relevant paths in the code to your own correct paths. ```bash # link ByteMLPerf and install requirements @@ -100,5 +113,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task roformer-tf-fp32 ## Results | Model | BatchSize | Precision | FPS | ACC | -| -------- | --------- | --------- | ------- | ------- | +|----------|-----------|-----------|---------|---------| | RoFormer | 2 | FP16 | 195.186 | 0.33789 | diff --git a/models/nlp/language_model/videobert/ixrt/README.md b/models/nlp/language_model/videobert/ixrt/README.md index 7bfb8c92b63203a3a726b83a47f573383c36db8f..cb3683845a1f676f2bb1b5882b495151413180a0 100644 --- a/models/nlp/language_model/videobert/ixrt/README.md +++ b/models/nlp/language_model/videobert/ixrt/README.md @@ -2,7 +2,9 @@ ## Description -VideoBERT is a model designed for video understanding tasks, extending the capabilities of BERT (Bidirectional Encoder Representations from Transformers) to video data. It enhances video representation learning by integrating both visual and textual information into a unified framework. +VideoBERT is a model designed for video understanding tasks, extending the capabilities of BERT (Bidirectional Encoder +Representations from Transformers) to video data. It enhances video representation learning by integrating both visual +and textual information into a unified framework. ## Setup @@ -73,5 +75,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task videobert-onnx-fp32 ## Results | Model | BatchSize | Precision | QPS | Top-1 ACC | -| --------- | --------- | --------- | ----- | --------- | +|-----------|-----------|-----------|-------|-----------| | VideoBERT | 4 | FP16 | 37.68 | 61.67 | diff --git a/models/nlp/large_language_model/baichuan2-7b/vllm/README.md b/models/nlp/large_language_model/baichuan2-7b/vllm/README.md index dab6b79d4b1791d3b5f92969fe2ba56be2348ba9..9a8cb1ec99d04c561e3a5baddf7a9e2e93add045 100755 --- a/models/nlp/large_language_model/baichuan2-7b/vllm/README.md +++ b/models/nlp/large_language_model/baichuan2-7b/vllm/README.md @@ -1,28 +1,33 @@ -# Baichuan-2-7B (vLLM) +# Baichuan2-7B (vLLM) ## Description -Baichuan 2 is a new generation open-source large language model launched by Baichuan Intelligence. It is trained on high-quality data with 26 trillion tokens, which sounds like a substantial dataset. Baichuan 2 achieves state-of-the-art performance on various authoritative Chinese, multilingual, and domain-specific benchmarks of similar size, indicating its excellent capabilities in language understanding and generation.This release includes Base and Chat versions of 7B. +Baichuan 2 is a new generation open-source large language model launched by Baichuan Intelligence. It is trained on +high-quality data with 26 trillion tokens, which sounds like a substantial dataset. Baichuan 2 achieves state-of-the-art +performance on various authoritative Chinese, multilingual, and domain-specific benchmarks of similar size, indicating +its excellent capabilities in language understanding and generation.This release includes Base and Chat versions of 7B. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install transformers ``` ### Download -Pretrained model: [https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main) +Pretrained model: +[https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main) ```bash mkdir /data/baichuan/ @@ -53,7 +58,7 @@ python3 offline_inference.py --model /data/baichuan/Baichuan2-7B-Base/int8/ --ch ## Results -| Model | Precision | tokens | QPS | -| ------------- | --------- | ------ | ------ | -| Baichuan-2-7B | FP16 | 768 | 109.27 | -| Baichuan-2-7B | w8a16 | 740 | 59.82 | +| Model | Precision | tokens | QPS | +|--------------|-----------|--------|--------| +| Baichuan2-7B | FP16 | 768 | 109.27 | +| Baichuan2-7B | w8a16 | 740 | 59.82 | diff --git a/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md b/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md index 57f754042d389808bea06e2fae0a06f7c7275806..c74b04fe49a27e6e13fc1e65727cead426d0dc3b 100644 --- a/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md +++ b/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md @@ -2,20 +2,25 @@ ## Description -ChatGLM3-6B-32K further enhances the understanding of long text capabilities based on ChatGLM3-6B, enabling better handling of contexts up to 32K in length. Specifically, we have updated the positional encoding and designed more targeted long text training methods, using a 32K context length during the training phase. In practical use, if your context length is mostly within 8K, we recommend using ChatGLM3-6B; if you need to handle context lengths exceeding 8K, we recommend using ChatGLM3-6B-32K. +ChatGLM3-6B-32K further enhances the understanding of long text capabilities based on ChatGLM3-6B, enabling better +handling of contexts up to 32K in length. Specifically, we have updated the positional encoding and designed more +targeted long text training methods, using a 32K context length during the training phase. In practical use, if your +context length is mostly within 8K, we recommend using ChatGLM3-6B; if you need to handle context lengths exceeding 8K, +we recommend using ChatGLM3-6B-32K. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install transformers ``` @@ -53,5 +58,5 @@ python3 server_inference.py --host 127.0.0.1 --port 12345 --model_path /data/cha ## Results | Model | Precision | tokens | QPS | -| --------------- | --------- | ------ | ------ | +|-----------------|-----------|--------|--------| | ChatGLM3-6B-32K | FP16 | 745 | 110.85 | diff --git a/models/nlp/large_language_model/chatglm3-6b/vllm/README.md b/models/nlp/large_language_model/chatglm3-6b/vllm/README.md index fb782cee26b31f7c680fd8328bd4040fe9f710f1..5dc174039af016912b9415e2bb61fd3409260ace 100644 --- a/models/nlp/large_language_model/chatglm3-6b/vllm/README.md +++ b/models/nlp/large_language_model/chatglm3-6b/vllm/README.md @@ -2,20 +2,23 @@ ## Description -ChatGLM3-6B is trained on large-scale natural language text data, enabling it to understand and generate text. It can be applied to various natural language processing tasks such as dialogue generation, text summarization, and language translation. +ChatGLM3-6B is trained on large-scale natural language text data, enabling it to understand and generate text. It can be +applied to various natural language processing tasks such as dialogue generation, text summarization, and language +translation. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install vllm pip3 install transformers @@ -79,4 +82,4 @@ python3 -m vllm.entrypoints.openai.api_server --model /data/chatglm/chatglm3-6b ```bash python3 benchmark_serving.py --host 127.0.0.1 --num-prompts 16 --model /data/chatglm/chatglm3-6b --dataset-name sharegpt \ --dataset-path /data/dataset/ShareGPT_V3_unfiltered_cleaned_split.json --sharegpt-output-len 130 --trust-remote-code -``` \ No newline at end of file +``` diff --git a/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md index af8b5abbcfc6eb665cb6f5a935dfa2bc5e3f1781..5b4751342f2b3646cced146562b68b93a66b8314 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepSeek-r1-distill-llama-70b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md index b5b9c6d0fd73099ac11bac6ebbc18f99c19201f5..45f291108eccc3e06e97816f71f12a6815a900da 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepSeek-r1-distill-llama-8b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md index 88eb51633c2fc459fccaada7df287349d3c89808..e10d00457fb4ecd10d344ebba96406d371196879 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-1.5b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md index f24c6904ade62b7028a17bf01bc4ef143d969935..27cade520fcd73e4a91c252b7303dc69cf4155d6 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-14b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md index 4a2b85bc6a9618d4a1d6543503bed7768dc7c919..bc3fab8935d73ecadc0f70da75ad4ac90dc747f6 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-32b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md index fba0db66aa7ea985df7b962be5b6ebb5cea59289..8cab93a6e79ead253a4f767c57a98a9a7d13fab2 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-7b/vllm diff --git a/models/nlp/large_language_model/llama2-13b/trtllm/README.md b/models/nlp/large_language_model/llama2-13b/trtllm/README.md index 3c36a98f50fb440e0287e9aa889014a049153da0..d525ad9fb31312898e8b51077fc1abdc3b785411 100755 --- a/models/nlp/large_language_model/llama2-13b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-13b/trtllm/README.md @@ -2,22 +2,23 @@ ## Description -The Llama2 model is part of the Llama project which aims to unlock the power of large language models. The latest version of the Llama model is now accessible to individuals, creators, researchers, and businesses of all sizes. It includes model weights and starting code for pre-trained and fine-tuned Llama language models with parameters ranging from 7B to 70B. +The Llama2 model is part of the Llama project which aims to unlock the power of large language models. The latest +version of the Llama model is now accessible to individuals, creators, researchers, and businesses of all sizes. It +includes model weights and starting code for pre-trained and fine-tuned Llama language models with parameters ranging +from 7B to 70B. ## Setup -### Instal +### Install ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev - +apt install -y libgl1-mesa-glx bash scripts/set_environment.sh . - ``` ### Download diff --git a/models/nlp/large_language_model/llama2-70b/trtllm/README.md b/models/nlp/large_language_model/llama2-70b/trtllm/README.md index 428dc7c5088b475692f7928d5b71c993f1da9f2e..628ee896e4b3a269dc91f9c7aa0e8b4a80e8ab60 100644 --- a/models/nlp/large_language_model/llama2-70b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-70b/trtllm/README.md @@ -2,7 +2,12 @@ ## Description -we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. +we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale +from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. +Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for +helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our +approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work +and contribute to the responsible development of LLMs. ## Setup @@ -13,16 +18,16 @@ we develop and release Llama 2, a collection of pretrained and fine-tuned large ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx bash scripts/set_environment.sh . ``` ### Download --Model: +- Model: --Dataset: +- Dataset: ```bash # Download model from the website and make sure the model's path is "data/llama2-70b-chat" diff --git a/models/nlp/large_language_model/llama2-7b/trtllm/README.md b/models/nlp/large_language_model/llama2-7b/trtllm/README.md index bcf06d484c67b8da6e663603eed0377989e58b1a..3be201f191d2a6178353a7c08cb7358d8b518c93 100644 --- a/models/nlp/large_language_model/llama2-7b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-7b/trtllm/README.md @@ -2,30 +2,35 @@ ## Description -we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. +we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale +from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. +Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for +helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our +approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work +and contribute to the responsible development of LLMs. ## Setup -### Instal +### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev - +apt install -y libgl1-mesa-glx bash scripts/set_environment.sh . ``` ### Download --Model: +- Model: --Dataset: +- Dataset: ```bash # Download model from the website and make sure the model's path is "data/llama2-7b-chat" diff --git a/models/nlp/large_language_model/llama2-7b/vllm/README.md b/models/nlp/large_language_model/llama2-7b/vllm/README.md index 5fa7012733feb811fcef8992355047a5bf7a14ee..44e6db7154fb262e573ffec154906451d53cd500 100755 --- a/models/nlp/large_language_model/llama2-7b/vllm/README.md +++ b/models/nlp/large_language_model/llama2-7b/vllm/README.md @@ -1,30 +1,37 @@ -# LlaMa2 7B (vLLM) +# Llama2 7B (vLLM) ## Description -we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. +we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale +from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. +Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for +helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our +approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work +and contribute to the responsible development of LLMs. ## Setup -### Instal +### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx # Contact the iluvatar manager to get adapted install packages of vllm, triton, and ixformer pip3 install vllm pip3 install triton pip3 install ixformer ``` + ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/llama2-7b/vllm diff --git a/models/nlp/large_language_model/llama3-70b/vllm/README.md b/models/nlp/large_language_model/llama3-70b/vllm/README.md index 8374dd94c6257e0c3e3732f3d543a97a192b2140..a6c4f4881a842b09c4e3d9ff8255f1a73a947573 100644 --- a/models/nlp/large_language_model/llama3-70b/vllm/README.md +++ b/models/nlp/large_language_model/llama3-70b/vllm/README.md @@ -2,9 +2,13 @@ ## Description -This model is the Meta Llama 3 large language model series (LLMs) released by Meta, which is a series of pre-trained and instruction-tuned generative text models, available in 8B and 70B models. The model is 70B in size and is designed for large-scale AI applications. +This model is the Meta Llama 3 large language model series (LLMs) released by Meta, which is a series of pre-trained and +instruction-tuned generative text models, available in 8B and 70B models. The model is 70B in size and is designed for +large-scale AI applications. -The Llama 3 command-tuned model is optimized for conversational use cases and outperforms many available open source chat models on common industry benchmarks. In addition, when developing these models, the research team paid great attention to optimizing beneficialness and safety. +The Llama 3 command-tuned model is optimized for conversational use cases and outperforms many available open source +chat models on common industry benchmarks. In addition, when developing these models, the research team paid great +attention to optimizing beneficialness and safety. Llama 3 is a major improvement over Llama 2 and other publicly available models: @@ -18,19 +22,20 @@ Llama 3 is a major improvement over Llama 2 and other publicly available models: ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/Meta-Llama-3-70B-Instruct" diff --git a/models/nlp/large_language_model/qwen-7b/vllm/README.md b/models/nlp/large_language_model/qwen-7b/vllm/README.md index 50c7cb095fc5b05b4efbad615bb7cd48652f47c8..7b2433b63f9f4da9d96ddc2600679c5f8447adaa 100644 --- a/models/nlp/large_language_model/qwen-7b/vllm/README.md +++ b/models/nlp/large_language_model/qwen-7b/vllm/README.md @@ -2,13 +2,25 @@ ## Description -Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models. +Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language +processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first +installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct +models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat +models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance +across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning +from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities +for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks +like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and +Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These +models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind +the proprietary models. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -25,7 +37,7 @@ pip3 install ixformer ### Download --Model: -Model: +- Model: - Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen-7b/vllm diff --git a/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md index d1792860528742d77f82b9a2f68a068038d78eb2..07741c3f62cc4fb0d0594b9aebf67fd1500893c0 100644 --- a/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-14b/vllm @@ -34,6 +38,6 @@ python3 offline_inference.py --model ./data/qwen1.5/Qwen1.5-14B --max-tokens 256 ## Results -| Model | QPS | -| ---------- | ----- | -| Qwen1.5-14B| 57.15 | +| Model | QPS | +|-------------|-------| +| Qwen1.5-14B | 57.15 | diff --git a/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md index 4e92d517cadcd16e4943e9d3689bc1d183fd8d17..97fdeca8ec3910a447292d6ff8e0b155031b6474 100755 --- a/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md @@ -2,7 +2,10 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. ## Setup @@ -23,7 +26,7 @@ pip3 install ixformer ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-32b/vllm diff --git a/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md index c9785998c8b8a196e59cb0df258a5aebe047e3d9..340eb83dbcfe85f6fb81af4c33f3dfdcc6f85c81 100644 --- a/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-72b/vllm @@ -35,6 +39,6 @@ python3 offline_inference.py --model ./data/qwen1.5/Qwen1.5-72B --max-tokens 256 ## Results -| Model | QPS | -| ---------- | ----- | -| Qwen1.5-72B| 41.24 | +| Model | QPS | +|-------------|-------| +| Qwen1.5-72B | 41.24 | diff --git a/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md b/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md index 41d34f2052f598deddb9cccfa41cdd367e9fad73..5df36b1bc86dba92509f2c73c1589bd886c6a37d 100644 --- a/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md +++ b/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference @@ -33,7 +37,13 @@ ln -s /path/to/Qwen1.5-7B ./data/qwen1.5 ```bash # Use one docker container to start webserver export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python -CUDA_VISIBLE_DEVICES=0 USE_FLASH_ATTENTION=true text-generation-launcher --model-id ./data/qwen1.5/Qwen1.5-7B --sharded false --dtype float16 --disable-custom-kernels --port 8001 --max-input-length 2048 --max-batch-prefill-tokens 2048 --max-total-tokens 4096 --max-batch-total-tokens 4096 +CUDA_VISIBLE_DEVICES=0 USE_FLASH_ATTENTION=true text-generation-launcher --model-id ./data/qwen1.5/Qwen1.5-7B \ + --sharded false --dtype float16 \ + --disable-custom-kernels \ + --port 8001 --max-input-length 2048 \ + --max-batch-prefill-tokens 2048 \ + --max-total-tokens 4096 \ + --max-batch-total-tokens 4096 ``` ### Offline test @@ -47,5 +57,5 @@ python3 offline_inference.py --model2path ./data/qwen1.5/Qwen1.5-7B ## Results | Model | QPS | -| ---------- | ----- | +|------------|-------| | Qwen1.5-7B | 39.11 | diff --git a/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md index 44f0c6b56fc30462118ad5862d023287c1d9ec65..d991b2d4ae361bbbf6b0f51a062a13186768017b 100644 --- a/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-7b/vllm @@ -34,6 +38,6 @@ python3 offline_inference.py --model ./data/qwen1.5/Qwen1.5-7B --max-tokens 256 ## Results -| Model | QPS | -| ---------- | ----- | -| Qwen1.5-7B | 109.56| +| Model | QPS | +|------------|--------| +| Qwen1.5-7B | 109.56 | diff --git a/models/nlp/large_language_model/qwen2-72b/vllm/README.md b/models/nlp/large_language_model/qwen2-72b/vllm/README.md index b859879019267a3cbee6eb90fae5d78c0b6fe306..08859625592449605bc8db75ccf133b3b3fb911d 100755 --- a/models/nlp/large_language_model/qwen2-72b/vllm/README.md +++ b/models/nlp/large_language_model/qwen2-72b/vllm/README.md @@ -2,11 +2,17 @@ ## Description -Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. +Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and +instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This +repo contains the instruction-tuned 72B Qwen2 model. -Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. +Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has +generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series +of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, +reasoning, etc. -Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to this section for detailed instructions on how to deploy Qwen2 for handling long texts. +Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. +Please refer to this section for detailed instructions on how to deploy Qwen2 for handling long texts. ## Setup @@ -27,7 +33,7 @@ pip3 install ixformer ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen2-72b/vllm diff --git a/models/nlp/large_language_model/qwen2-7b/vllm/README.md b/models/nlp/large_language_model/qwen2-7b/vllm/README.md index dce3c564b8c201ebee24149b0ca98b54e0bbd3b1..fb1556b094789cf9bf6db147fd89207365808aeb 100755 --- a/models/nlp/large_language_model/qwen2-7b/vllm/README.md +++ b/models/nlp/large_language_model/qwen2-7b/vllm/README.md @@ -2,9 +2,14 @@ ## Description -Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model. +Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and +instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This +repo contains the instruction-tuned 7B Qwen2 model. -Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. +Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has +generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series +of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, +reasoning, etc. Qwen2-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. @@ -27,10 +32,10 @@ pip3 install ixformer ### Download --Model: https://modelscope.cn/models/Qwen/Qwen2-7B-Instruct +- Model: https://modelscope.cn/models/Qwen/Qwen2-7B-Instruct ```bash -cd ${DeepSparkInference}/models/nlp/large_language_model/qwen2-7b/vllm +cd models/nlp/large_language_model/qwen2-7b/vllm mkdir -p data/qwen2 ln -s /path/to/Qwen2-7B-Instruct ./data/qwen2 ``` diff --git a/models/nlp/large_language_model/stablelm/vllm/README.md b/models/nlp/large_language_model/stablelm/vllm/README.md index 18fd9a8f6a027e78861a0fa7ce31165e9ba019d2..bea23654c5d83ca9fd875ad94738b5db5a0d61bf 100644 --- a/models/nlp/large_language_model/stablelm/vllm/README.md +++ b/models/nlp/large_language_model/stablelm/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Stable LM 2 1.6B is a decoder-only language model with 1.6 billion parameters. It has been pre-trained on a diverse multilingual and code dataset, comprising 2 trillion tokens, for two epochs. This model is designed for various natural language processing tasks, including text generation and dialogue systems. Due to its extensive training on such a large and diverse dataset, Stable LM 2 1.6B can effectively capture the nuances of language, including grammar, semantics, and contextual relationships, which enhances the quality and accuracy of the generated text. +Stable LM 2 1.6B is a decoder-only language model with 1.6 billion parameters. It has been pre-trained on a diverse +multilingual and code dataset, comprising 2 trillion tokens, for two epochs. This model is designed for various natural +language processing tasks, including text generation and dialogue systems. Due to its extensive training on such a large +and diverse dataset, Stable LM 2 1.6B can effectively capture the nuances of language, including grammar, semantics, and +contextual relationships, which enhances the quality and accuracy of the generated text. ## Setup @@ -19,7 +23,7 @@ pip3 install transformers ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/stablelm/stablelm-2-1_6b" @@ -36,5 +40,5 @@ python3 offline_inference.py --model ./data/stablelm/stablelm-2-1_6b --max-token ## Results | Model | QPS | -| -------- | ----- | +|----------|-------| | StableLM | 254.3 |